diff --git a/api/.buildinfo b/api/.buildinfo
index b5cf5bebb..969bee2be 100644
--- a/api/.buildinfo
+++ b/api/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 0555596bae49f971011984d58e3c9fcc
+config: 67b59008c58998f711ccce32ad07bc46
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/api/_modules/cyclops/data/aggregate.html b/api/_modules/cyclops/data/aggregate.html
index 3bfe49b62..e0be3dbb1 100644
--- a/api/_modules/cyclops/data/aggregate.html
+++ b/api/_modules/cyclops/data/aggregate.html
@@ -60,7 +60,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/data/features/medical_image.html b/api/_modules/cyclops/data/features/medical_image.html
index 3ed3d29a8..7a2fdabe2 100644
--- a/api/_modules/cyclops/data/features/medical_image.html
+++ b/api/_modules/cyclops/data/features/medical_image.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/data/slicer.html b/api/_modules/cyclops/data/slicer.html
index 0e43c6edc..5a2bd5d34 100644
--- a/api/_modules/cyclops/data/slicer.html
+++ b/api/_modules/cyclops/data/slicer.html
@@ -60,7 +60,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/evaluator.html b/api/_modules/cyclops/evaluate/evaluator.html
index 97f92317e..9c8f221ec 100644
--- a/api/_modules/cyclops/evaluate/evaluator.html
+++ b/api/_modules/cyclops/evaluate/evaluator.html
@@ -60,7 +60,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/fairness/evaluator.html b/api/_modules/cyclops/evaluate/fairness/evaluator.html
index ce776842f..5f63d6983 100644
--- a/api/_modules/cyclops/evaluate/fairness/evaluator.html
+++ b/api/_modules/cyclops/evaluate/fairness/evaluator.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/accuracy.html b/api/_modules/cyclops/evaluate/metrics/accuracy.html
index 205e2d7be..20ca41840 100644
--- a/api/_modules/cyclops/evaluate/metrics/accuracy.html
+++ b/api/_modules/cyclops/evaluate/metrics/accuracy.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/auroc.html b/api/_modules/cyclops/evaluate/metrics/auroc.html
index 1f19f0f8e..e2dd3572b 100644
--- a/api/_modules/cyclops/evaluate/metrics/auroc.html
+++ b/api/_modules/cyclops/evaluate/metrics/auroc.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/average_precision.html b/api/_modules/cyclops/evaluate/metrics/average_precision.html
index e112f5163..79f09977b 100644
--- a/api/_modules/cyclops/evaluate/metrics/average_precision.html
+++ b/api/_modules/cyclops/evaluate/metrics/average_precision.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/f_beta.html b/api/_modules/cyclops/evaluate/metrics/f_beta.html
index 9b34230ef..dc4f4a39c 100644
--- a/api/_modules/cyclops/evaluate/metrics/f_beta.html
+++ b/api/_modules/cyclops/evaluate/metrics/f_beta.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/functional/accuracy.html b/api/_modules/cyclops/evaluate/metrics/functional/accuracy.html
index 6d911d4fd..2776c015b 100644
--- a/api/_modules/cyclops/evaluate/metrics/functional/accuracy.html
+++ b/api/_modules/cyclops/evaluate/metrics/functional/accuracy.html
@@ -60,7 +60,6 @@
     <script src="../../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/functional/auroc.html b/api/_modules/cyclops/evaluate/metrics/functional/auroc.html
index beba98645..f40b72e42 100644
--- a/api/_modules/cyclops/evaluate/metrics/functional/auroc.html
+++ b/api/_modules/cyclops/evaluate/metrics/functional/auroc.html
@@ -60,7 +60,6 @@
     <script src="../../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/functional/average_precision.html b/api/_modules/cyclops/evaluate/metrics/functional/average_precision.html
index 7dabab4a7..109263cbd 100644
--- a/api/_modules/cyclops/evaluate/metrics/functional/average_precision.html
+++ b/api/_modules/cyclops/evaluate/metrics/functional/average_precision.html
@@ -60,7 +60,6 @@
     <script src="../../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/functional/f_beta.html b/api/_modules/cyclops/evaluate/metrics/functional/f_beta.html
index e5895a1f4..be40bc779 100644
--- a/api/_modules/cyclops/evaluate/metrics/functional/f_beta.html
+++ b/api/_modules/cyclops/evaluate/metrics/functional/f_beta.html
@@ -60,7 +60,6 @@
     <script src="../../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/functional/precision_recall.html b/api/_modules/cyclops/evaluate/metrics/functional/precision_recall.html
index 17acdc5a5..1aff6b0b2 100644
--- a/api/_modules/cyclops/evaluate/metrics/functional/precision_recall.html
+++ b/api/_modules/cyclops/evaluate/metrics/functional/precision_recall.html
@@ -60,7 +60,6 @@
     <script src="../../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/functional/precision_recall_curve.html b/api/_modules/cyclops/evaluate/metrics/functional/precision_recall_curve.html
index e1df790e2..cdae16824 100644
--- a/api/_modules/cyclops/evaluate/metrics/functional/precision_recall_curve.html
+++ b/api/_modules/cyclops/evaluate/metrics/functional/precision_recall_curve.html
@@ -60,7 +60,6 @@
     <script src="../../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/functional/roc.html b/api/_modules/cyclops/evaluate/metrics/functional/roc.html
index 6a4cb4c17..f99711902 100644
--- a/api/_modules/cyclops/evaluate/metrics/functional/roc.html
+++ b/api/_modules/cyclops/evaluate/metrics/functional/roc.html
@@ -60,7 +60,6 @@
     <script src="../../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/functional/sensitivity.html b/api/_modules/cyclops/evaluate/metrics/functional/sensitivity.html
index 3f2b091ae..f701a91ae 100644
--- a/api/_modules/cyclops/evaluate/metrics/functional/sensitivity.html
+++ b/api/_modules/cyclops/evaluate/metrics/functional/sensitivity.html
@@ -60,7 +60,6 @@
     <script src="../../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/functional/specificity.html b/api/_modules/cyclops/evaluate/metrics/functional/specificity.html
index 18a197f8b..f989d3b0d 100644
--- a/api/_modules/cyclops/evaluate/metrics/functional/specificity.html
+++ b/api/_modules/cyclops/evaluate/metrics/functional/specificity.html
@@ -60,7 +60,6 @@
     <script src="../../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/functional/stat_scores.html b/api/_modules/cyclops/evaluate/metrics/functional/stat_scores.html
index eacd7344c..7892d8453 100644
--- a/api/_modules/cyclops/evaluate/metrics/functional/stat_scores.html
+++ b/api/_modules/cyclops/evaluate/metrics/functional/stat_scores.html
@@ -60,7 +60,6 @@
     <script src="../../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/precision_recall.html b/api/_modules/cyclops/evaluate/metrics/precision_recall.html
index c5683fb92..93095c95d 100644
--- a/api/_modules/cyclops/evaluate/metrics/precision_recall.html
+++ b/api/_modules/cyclops/evaluate/metrics/precision_recall.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/precision_recall_curve.html b/api/_modules/cyclops/evaluate/metrics/precision_recall_curve.html
index b1c9ec079..ad56484f5 100644
--- a/api/_modules/cyclops/evaluate/metrics/precision_recall_curve.html
+++ b/api/_modules/cyclops/evaluate/metrics/precision_recall_curve.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/roc.html b/api/_modules/cyclops/evaluate/metrics/roc.html
index bea090e9f..09d59b291 100644
--- a/api/_modules/cyclops/evaluate/metrics/roc.html
+++ b/api/_modules/cyclops/evaluate/metrics/roc.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/sensitivity.html b/api/_modules/cyclops/evaluate/metrics/sensitivity.html
index 49c91bb63..4b28ba805 100644
--- a/api/_modules/cyclops/evaluate/metrics/sensitivity.html
+++ b/api/_modules/cyclops/evaluate/metrics/sensitivity.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/specificity.html b/api/_modules/cyclops/evaluate/metrics/specificity.html
index 3d23058cc..8a0978203 100644
--- a/api/_modules/cyclops/evaluate/metrics/specificity.html
+++ b/api/_modules/cyclops/evaluate/metrics/specificity.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/evaluate/metrics/stat_scores.html b/api/_modules/cyclops/evaluate/metrics/stat_scores.html
index 805644d43..aa5dddb5d 100644
--- a/api/_modules/cyclops/evaluate/metrics/stat_scores.html
+++ b/api/_modules/cyclops/evaluate/metrics/stat_scores.html
@@ -60,7 +60,6 @@
     <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/monitor/clinical_applicator.html b/api/_modules/cyclops/monitor/clinical_applicator.html
index ab036222c..c8039518b 100644
--- a/api/_modules/cyclops/monitor/clinical_applicator.html
+++ b/api/_modules/cyclops/monitor/clinical_applicator.html
@@ -60,7 +60,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/monitor/synthetic_applicator.html b/api/_modules/cyclops/monitor/synthetic_applicator.html
index e95b0e2cf..b68a6f8b2 100644
--- a/api/_modules/cyclops/monitor/synthetic_applicator.html
+++ b/api/_modules/cyclops/monitor/synthetic_applicator.html
@@ -60,7 +60,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/report/report.html b/api/_modules/cyclops/report/report.html
index d75400d58..94e37c02a 100644
--- a/api/_modules/cyclops/report/report.html
+++ b/api/_modules/cyclops/report/report.html
@@ -60,7 +60,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/cyclops/tasks/classification.html b/api/_modules/cyclops/tasks/classification.html
index 63085a8d7..25b3ec041 100644
--- a/api/_modules/cyclops/tasks/classification.html
+++ b/api/_modules/cyclops/tasks/classification.html
@@ -60,7 +60,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_modules/index.html b/api/_modules/index.html
index e1e7f9935..23be9d4b8 100644
--- a/api/_modules/index.html
+++ b/api/_modules/index.html
@@ -60,7 +60,6 @@
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/require.min.js?v=53367eec"></script>
     <script src="../_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/_sources/examples/metrics.ipynb.txt b/api/_sources/examples/metrics.ipynb.txt
deleted file mode 100644
index cf2b10ba4..000000000
--- a/api/_sources/examples/metrics.ipynb.txt
+++ /dev/null
@@ -1,351 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Breast Cancer Classification and Evaluation\n",
-    "\n",
-    "The Breast Cancer dataset is a well-suited example for demonstrating CyclOps features due to its two distinct classes (binary classification) and complete absence of missing values. This clean and organized structure makes it an ideal starting point for exploring CyclOps Evaluator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from datasets.arrow_dataset import Dataset\n",
-    "from sklearn import datasets\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate import evaluator\n",
-    "from cyclops.evaluate.fairness import evaluate_fairness\n",
-    "from cyclops.evaluate.metrics import BinaryAccuracy, create_metric\n",
-    "from cyclops.evaluate.metrics.experimental import BinaryAUROC, BinaryAveragePrecision\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Loading the data\n",
-    "breast_cancer_data = datasets.load_breast_cancer(as_frame=True)\n",
-    "X, y = breast_cancer_data.data, breast_cancer_data.target"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Features\n",
-    "Just taking a quick look at features and their stats..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = breast_cancer_data.frame\n",
-    "df.describe().T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Splitting into train and test\n",
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    X,\n",
-    "    y,\n",
-    "    test_size=0.1,\n",
-    "    random_state=13,\n",
-    ")\n",
-    "\n",
-    "# Use SVM classifier for binary classification\n",
-    "svc = SVC(C=10, gamma=0.01, probability=True)\n",
-    "svc.fit(X_train, y_train)\n",
-    "\n",
-    "# model predictions\n",
-    "y_pred = svc.predict(X_test)\n",
-    "y_pred_prob = svc.predict_proba(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use CyclOps evaluation metrics to evaluate our model's performance. You can either use each metric individually by calling them, or define a ``MetricDict`` object.\n",
-    "Here, we show both methods."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Individual Metrics\n",
-    "In case you need only a single metric, you can create an object of the desired metric and call it on your ground truth and predictions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bin_acc_metric = BinaryAccuracy()\n",
-    "bin_acc_metric(y_test.values, np.float64(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using ``MetricDict``\n",
-    "You may define a collection of metrics in case you need more metrics. It also speeds up the metric calculation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_roc_curve\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)\n",
-    "metric_collection(y_test.values, np.float64(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You may reset the metrics collection and add other metrics:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_collection.reset()\n",
-    "metric_collection.add_metrics(BinaryAveragePrecision(), BinaryAUROC())\n",
-    "metric_collection(y_test.values, np.float64(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data Slicing\n",
-    "\n",
-    "In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulation or subsets. We can define these subsets using ``SliceSpec`` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"worst radius\": {\n",
-    "            \"min_value\": 14.0,\n",
-    "            \"max_value\": 15.0,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"worst radius\": {\n",
-    "            \"min_value\": 15.0,\n",
-    "            \"max_value\": 17.0,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"worst texture\": {\n",
-    "            \"min_value\": 23.1,\n",
-    "            \"max_value\": 28.7,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Intersectional slicing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When subpopulation slices are specified using the ``SliceSpec``, sometimes we wish create combinations of intersectional slices. We can use the ``intersections`` argument to specify this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "slice_spec = SliceSpec(spec_list, intersections=2)\n",
-    "slice_spec"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Preparing Result\n",
-    "\n",
-    "CyclOps Evaluator takes data as a HuggingFace Dataset object, so we combine predictions and features in a dataframe, and create a `Dataset` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Combine result and features for test data\n",
-    "df = pd.concat([X_test, pd.DataFrame(y_test, columns=[\"target\"])], axis=1)\n",
-    "df[\"preds\"] = y_pred\n",
-    "df[\"preds_prob\"] = y_pred_prob[:, 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Dataset object\n",
-    "breast_cancer_data = Dataset.from_pandas(df)\n",
-    "breast_cancer_sliced_result = evaluator.evaluate(\n",
-    "    dataset=breast_cancer_data,\n",
-    "    metrics=metric_collection,  # type: ignore[list-item]\n",
-    "    target_columns=\"target\",\n",
-    "    prediction_columns=\"preds_prob\",\n",
-    "    slice_spec=slice_spec,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can visualize the ``BinaryF1Score`` and ``BinaryPrecision`` for the different slices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name in [\"BinaryF1Score\", \"BinaryPrecision\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in breast_cancer_sliced_result[\n",
-    "        \"model_for_preds_prob\"\n",
-    "    ].items()\n",
-    "}\n",
-    "# Plotting the metric values for all the slices.\n",
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Fairness Evaluator\n",
-    "\n",
-    "The Breast Cancer dataset may not be a very good example to apply fairness, but to demonstrate how you can use our fairness evaluator, we apply it to `mean texture` feature. It's recommended to use it on features with discrete values. For optimal results, the feature should have less than 50 unique categories."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fairness_result = evaluate_fairness(\n",
-    "    dataset=breast_cancer_data,\n",
-    "    metrics=\"binary_precision\",  # type: ignore[list-item]\n",
-    "    groups=\"mean texture\",\n",
-    "    target_columns=\"target\",\n",
-    "    prediction_columns=\"preds_prob\",\n",
-    ")\n",
-    "fairness_result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/api/_sources/examples/report.ipynb.txt b/api/_sources/examples/report.ipynb.txt
deleted file mode 100644
index 6312ba6f5..000000000
--- a/api/_sources/examples/report.ipynb.txt
+++ /dev/null
@@ -1,900 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import copy\n",
-    "import inspect\n",
-    "import os\n",
-    "import shutil\n",
-    "from datetime import date\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "from datasets import Dataset\n",
-    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate.metrics import create_metric\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.report import ModelCardReport\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter\n",
-    "from cyclops.report.utils import flatten_results_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Report Generation for Heart Failure Prediction\n",
-    "Here's an example to demonstrate how we can generate a report as we proceed through all the steps to train and evaluate a model. For this purpose, we are going to use Kaggle's heart prediction failure dataset and gradually populate the report with information about dataset, model and results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Model Card Report\n",
-    "First, we should create a `ModelCardReport` object to fill in the fields and sections after training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Constants\n",
-    "DATA_DIR = \"./data\"\n",
-    "RANDOM_SEED = 21"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Loading\n",
-    "Before starting, make sure to install the Kaggle API by running pip install kaggle. To use the Kaggle API, you need to sign up for a Kaggle account at https://www.kaggle.com. Then go to the ‘Account’ tab of your user profile (https://www.kaggle.com/<username>/account) and select ‘Create API Token’. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location ~/.kaggle/kaggle.json on your machine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "api = KaggleApi()\n",
-    "api.authenticate()\n",
-    "api.dataset_download_files(\n",
-    "    \"fedesoriano/heart-failure-prediction\",\n",
-    "    path=DATA_DIR,\n",
-    "    unzip=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(os.path.join(DATA_DIR, \"heart.csv\"))\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.describe().T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"Sex\")\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    title=\"Sex Distribution\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adding figures to report\n",
-    "We can add figures and diagrams to report. We can define caption and the section of the report that this figure belongs to. Since we are exploring the distribution of different features in the dataset, we add it to `datasets` section:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Sex Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Age distribution figure\n",
-    "We plot a histogram of ages similarly and add the figure to our report. This will appear under **Dataset** section:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(df, x=\"Age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Outcome distribution\n",
-    "Plot outcome distribution and add it to report. Similarly, we also place it under **Dataset** section:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"outcome\"] = df[\"HeartDisease\"].astype(\"int\")\n",
-    "df = df.drop(columns=[\"HeartDisease\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"outcome\")\n",
-    "fig.update_traces(textinfo=\"percent+label\")\n",
-    "fig.update_layout(title_text=\"Outcome Distribution\")\n",
-    "fig.update_traces(\n",
-    "    hovertemplate=\"Outcome: %{label}<br>Count: \\\n",
-    "    %{value}<br>Percent: %{percent}\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Outcome Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_counts = df[\"outcome\"].value_counts()\n",
-    "class_ratio = class_counts[0] / class_counts[1]\n",
-    "print(class_ratio, class_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This dataset does not have any null values, so we can jump to feature scaling. The string data in the dataframe is in the form of object, we need to convert it back to string to work on it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "string_col = df.select_dtypes(include=\"object\").columns\n",
-    "df[string_col] = df[string_col].astype(\"string\")\n",
-    "string_col = df.select_dtypes(\"string\").columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are going to apply tree-based models to our data, so we use `LabelEncoder`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target = \"outcome\"\n",
-    "\n",
-    "df_processed = pd.get_dummies(df, columns=string_col, drop_first=False)\n",
-    "df_processed.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_cols = df_processed.columns.to_list()\n",
-    "feature_cols.remove(target)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before training, let's document dataset in the model card.\n",
-    "This can be done using the log_dataset method, which takes the following arguments: \n",
-    "\n",
-    "- `description`: A description of the dataset. \n",
-    "- `citation`: The citation for the dataset. \n",
-    "- `link`: A link to a resource for the dataset. \n",
-    "- `license_id`: The SPDX license identifier for the dataset. \n",
-    "- `version`: The version of the dataset. \n",
-    "- `features`: A list of features in the dataset. \n",
-    "- `split`: The split of the dataset (train, test, validation, etc.). \n",
-    "- `sensitive_features`: A list of sensitive features used to train/evaluate the model. \n",
-    "- `sensitive_feature_justification`: A justification for the sensitive features used to train/evaluate the model.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_dataset(\n",
-    "    description=\"\"\"This dataset was created by combining different datasets\n",
-    "    already available independently but not combined before. In this dataset,\n",
-    "    5 heart datasets are combined over 11 common features. Every dataset used\n",
-    "    can be found under the Index of heart disease datasets from UCI\n",
-    "    Machine Learning Repository on the following link:\n",
-    "    https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/.\"\"\",\n",
-    "    citation=inspect.cleandoc(\n",
-    "        \"\"\"\n",
-    "        @misc{fedesoriano,\n",
-    "          title={Heart Failure Prediction Dataset.},\n",
-    "          author={Fedesoriano, F},\n",
-    "          year={2021},\n",
-    "          publisher={Kaggle}\n",
-    "        }\n",
-    "    \"\"\",\n",
-    "    ),\n",
-    "    link=\"\"\"\n",
-    "    https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction\n",
-    "    \"\"\",\n",
-    "    license_id=\"CC0-1.0\",\n",
-    "    version=\"Version 1\",\n",
-    "    features=df.columns.to_list().remove(target),\n",
-    "    sensitive_features=[\"Sex\", \"Age\"],\n",
-    "    sensitive_feature_justification=\"Demographic information like age and gender \\\n",
-    "        often have a strong correlation with health outcomes. For example, older \\\n",
-    "        patients are more likely to have a higher risk of heart disease.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create HuggingFace Dataset\n",
-    "We convert our processed Pandas dataframe into a Hugging Face dataset, for later evaluation by Cyclop metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset = Dataset.from_pandas(df_processed)\n",
-    "dataset.cleanup_cache_files()\n",
-    "print(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Split dataframe into inputs and outputs\n",
-    "X, y = df_processed[feature_cols], df_processed[target]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Splitting into train and test\n",
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    X,\n",
-    "    y,\n",
-    "    test_size=0.2,\n",
-    "    random_state=RANDOM_SEED,\n",
-    ")\n",
-    "classifier = LogisticRegression()\n",
-    "scaler = MinMaxScaler()\n",
-    "X_train = scaler.fit_transform(X_train)\n",
-    "X_test = scaler.fit_transform(X_test)\n",
-    "classifier.fit(X_train, y_train)\n",
-    "y_pred = classifier.predict(X_test)\n",
-    "y_pred_prob = classifier.predict_proba(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluation\n",
-    "As demonstrated in evaluation tutorial, we define a metric dict:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_roc_curve\",\n",
-    "    \"binary_auroc\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)\n",
-    "metric_collection(y_test.values, np.float64(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Slicing\n",
-    "In addition to overall metrics, it might be useful to see how the model performs on certain subpopulation or subsets. We can define these subsets using SliceSpec objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"Age\": {\n",
-    "            \"min_value\": 30,\n",
-    "            \"max_value\": 50,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"Age\": {\n",
-    "            \"min_value\": 50,\n",
-    "            \"max_value\": 70,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below, we are combining the raw features of the test data and the predictions so that we use them later for slice-specific evaluations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get positions of matching indices in df\n",
-    "matching_positions = y_test.index.get_indexer(df.index)\n",
-    "\n",
-    "# Select rows from df using matching positions (valid positions are non-negative)\n",
-    "df_test = df.iloc[matching_positions[matching_positions >= 0]]\n",
-    "df_test[\"preds\"] = y_pred\n",
-    "df_test[\"preds_prob\"] = y_pred_prob[:, 1]\n",
-    "df_test.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Age distribution in test data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(df_test, x=\"Age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution in Test Data\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Logging metrics and results to report\n",
-    "Here, we gather evalutaions and add them to the report.\n",
-    "\n",
-    "We can add a performance metric to the model card using the `log_performance_metric` method, which expects a dictionary where the keys are in the following format: `slice_name/metric_name`. For instance, `overall/accuracy` or `Age:[30 - 50)/BinaryPrecision`.\n",
-    "\n",
-    "We first need to process the evaluation results to get the metrics in the right format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from cyclops.evaluate import evaluator\n",
-    "\n",
-    "\n",
-    "# Create Dataset object\n",
-    "heart_failure_data = Dataset.from_pandas(df_test)\n",
-    "\n",
-    "result = evaluator.evaluate(\n",
-    "    dataset=heart_failure_data,\n",
-    "    metrics=metric_collection,  # type: ignore[list-item]\n",
-    "    target_columns=target,\n",
-    "    prediction_columns=\"preds_prob\",\n",
-    "    slice_spec=slice_spec,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = flatten_results_dict(\n",
-    "    results=result, remove_metrics=[\"BinaryROC\"], model_name=\"model_for_preds_prob\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We first need to process the evaluation results to get the metrics in the right format. The descriptions dictionary will appear as you hover on metrics in the report, so feel free to change them as it's appropriate for your usage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the `ClassificationPlotter`(as demonstrated in Evaluation example) to plot the performance metrics and the add the figure to the model card using the log_plotly_figure method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the ROC curves and AUROC results for all the slices\n",
-    "model_name = \"model_for_preds_prob\"\n",
-    "roc_curves = {\n",
-    "    slice_name: slice_results[\"BinaryROC\"]\n",
-    "    for slice_name, slice_results in result[model_name].items()\n",
-    "}\n",
-    "aurocs = {\n",
-    "    slice_name: slice_results[\"BinaryAUROC\"]\n",
-    "    for slice_name, slice_results in result[model_name].items()\n",
-    "}\n",
-    "\n",
-    "# plotting the ROC curves for all the slices\n",
-    "roc_plot = plotter.roc_curve_comparison(roc_curves, aurocs=aurocs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=roc_plot,\n",
-    "    caption=\"ROC Curve for All Patients\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "roc_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the overall classification metric values.\n",
-    "overall_performance = {\n",
-    "    metric_name: metric_value\n",
-    "    for metric_name, metric_value in result[model_name][\"overall\"].items()\n",
-    "    if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "}\n",
-    "# Plotting the overall classification metric values.\n",
-    "overall_performance_plot = plotter.metrics_value(\n",
-    "    overall_performance,\n",
-    "    title=\"Overall Performance\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=overall_performance_plot,\n",
-    "    caption=\"Overall Performance\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "overall_performance_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in result[model_name].items()\n",
-    "}\n",
-    "# Plotting the metric values for all the slices.\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=slice_metrics_plot,\n",
-    "    caption=\"Slice Metric Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Report Generation\n",
-    "\n",
-    "### ModelCard\n",
-    "First, let's document the model details section. A **ModelCard** has several **Sections** and each **Section** includes multiple **Fields**. Model details could be one of the sections in our model card, and it has the following fields by default:\n",
-    "\n",
-    "- **`description`**: A high-level description of the model and its usage for a general audience. \n",
-    "- **`version`**: The version of the model. \n",
-    "- **`owners`**: The individuals or organizations that own the model. \n",
-    "- **`license`**: The license under which the model is made available. \n",
-    "- **`citation`**: The citation for the model. \n",
-    "- **`references`**: Links to resources that are relevant to the model. \n",
-    "- **`path`**: The path to where the model is stored. \n",
-    "- **`regulatory_requirements`**: The regulatory requirements that are relevant to the model.\n",
-    "\n",
-    "We can add additional fields to the model details section by passing a dictionary to the `log_from_dict` method and specifying the section name as `model_details`. You can also use the `log_descriptor` method to add a new field object with a description attribute to any section of the model card."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"Heart Failure Prediction Model\",\n",
-    "        \"description\": \"The model was trained on the Kaggle Heart Failure \\\n",
-    "        Prediction Dataset to predict risk of heart failure.\",\n",
-    "    },\n",
-    "    section_name=\"model_details\",\n",
-    ")\n",
-    "\n",
-    "report.log_version(\n",
-    "    version_str=\"0.0.1\",\n",
-    "    date=str(date.today()),\n",
-    "    description=\"Initial Release\",\n",
-    ")\n",
-    "report.log_owner(\n",
-    "    name=\"CyclOps Team\",\n",
-    "    contact=\"vectorinstitute.github.io/cyclops/\",\n",
-    "    email=\"cyclops@vectorinstitute.ai\",\n",
-    ")\n",
-    "report.log_license(identifier=\"Apache-2.0\")\n",
-    "report.log_reference(\n",
-    "    link=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\",  # noqa: E501\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Considerations\n",
-    "\n",
-    "Next, let’s populate the considerations section, which includes the following fields by default: \n",
-    "- **`users`**: The intended users of the model. \n",
-    "- **`use_cases`**: The use cases for the model. These could be primary, downstream or out-of-scope use cases. \n",
-    "- **`fairness_assessment`**: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms. \n",
-    "- **`ethical_considerations`**: The risks associated with using the model and the steps taken to mitigate them. This can be populated using the log_risk method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"users\": [\n",
-    "            {\"description\": \"Hospitals\"},\n",
-    "            {\"description\": \"Clinicians\"},\n",
-    "        ],\n",
-    "    },\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_user(description=\"ML Engineers\")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of heart failure.\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of pathologies and conditions other\\\n",
-    "    than heart failure.\",\n",
-    "    kind=\"out-of-scope\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"sex, age\",\n",
-    "    benefit=\"Improved health outcomes for patients.\",\n",
-    "    harm=\"Biased predictions for patients in certain groups (e.g. older patients) \\\n",
-    "        may lead to worse health outcomes.\",\n",
-    "    mitigation_strategy=\"We will monitor the performance of the model on these groups \\\n",
-    "        and retrain the model if the performance drops below a certain threshold.\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"The model may be used to make decisions that affect the health of patients.\",\n",
-    "    mitigation_strategy=\"The model should be continuously monitored for performance \\\n",
-    "        and retrained if the performance drops below a certain threshold.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exporting report\n",
-    "Once the model card is populated, you can generate the report using the `export` method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named `cyclops_reports` in the current working directory. You can change the path by passing a `output_dir` argument when instantiating the `ModelCardReport` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(42)\n",
-    "\n",
-    "synthetic_timestamps = pd.date_range(\n",
-    "    start=\"1/1/2020\", periods=10, freq=\"D\"\n",
-    ").values.astype(str)\n",
-    "\n",
-    "\n",
-    "report._model_card.overview = None\n",
-    "report_path = report.export(\n",
-    "    output_filename=\"heart_failure_report_periodic.html\",\n",
-    "    synthetic_timestamp=synthetic_timestamps[0],\n",
-    "    last_n_evals=3,\n",
-    ")\n",
-    "\n",
-    "shutil.copy(f\"{report_path}\", \".\")\n",
-    "metric_save = None\n",
-    "for i in tqdm(range(len(synthetic_timestamps[1:]))):\n",
-    "    if i == 3:\n",
-    "        report._model_card.quantitative_analysis.performance_metrics.append(\n",
-    "            metric_save,\n",
-    "        )\n",
-    "    report._model_card.overview = None\n",
-    "    for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "        metric.value = np.clip(\n",
-    "            metric.value + np.random.normal(0, 0.1),\n",
-    "            0,\n",
-    "            1,\n",
-    "        )\n",
-    "        metric.tests[0].passed = bool(metric.value >= 0.7)\n",
-    "    if i == 2:\n",
-    "        metrics = []\n",
-    "        for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "            if metric.type == \"BinaryAccuracy\" and metric.slice == \"Age:[30 - 50)\":\n",
-    "                metric_save = copy.deepcopy(metric)\n",
-    "            else:\n",
-    "                metrics.append(metric)\n",
-    "        report._model_card.quantitative_analysis.performance_metrics = metrics\n",
-    "    report_path = report.export(\n",
-    "        output_filename=\"heart_failure_report_periodic.html\",\n",
-    "        synthetic_timestamp=synthetic_timestamps[i + 1],\n",
-    "        last_n_evals=3,\n",
-    "    )\n",
-    "    shutil.copy(f\"{report_path}\", \".\")\n",
-    "shutil.rmtree(\"./cyclops_report\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You're now able to view the report [report](./heart_failure_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cyclops",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/api/_sources/tutorials/diabetes_130/readmission_prediction.ipynb.txt b/api/_sources/tutorials/diabetes_130/readmission_prediction.ipynb.txt
deleted file mode 100644
index 4e50a97d1..000000000
--- a/api/_sources/tutorials/diabetes_130/readmission_prediction.ipynb.txt
+++ /dev/null
@@ -1,1417 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Readmission Prediction\n",
-    "\n",
-    "This notebook showcases readmission prediction on the [Diabetes 130-US Hospitals for Years 1999-2008](https://archive.ics.uci.edu/dataset/296/diabetes+130-us+hospitals+for+years+1999-2008) using CyclOps. The task is formulated as a binary classification task, where we predict the probability of early readmission of the patient within 30 days of discharge."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Install libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install pycyclops\n",
-    "!pip install ucimlrepo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"Readmission prediction.\"\"\"\n",
-    "\n",
-    "# ruff: noqa: E402\n",
-    "\n",
-    "import copy\n",
-    "import inspect\n",
-    "from datetime import date\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "from datasets import Dataset\n",
-    "from datasets.features import ClassLabel\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
-    "from ucimlrepo import fetch_ucirepo\n",
-    "\n",
-    "from cyclops.data.df.feature import TabularFeatures\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate.fairness import FairnessConfig  # noqa: E402\n",
-    "from cyclops.evaluate.metrics import create_metric\n",
-    "from cyclops.evaluate.metrics.experimental.functional import (\n",
-    "    binary_npv,\n",
-    "    binary_ppv,\n",
-    "    binary_roc,\n",
-    ")\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.models.catalog import create_model\n",
-    "from cyclops.report import ModelCardReport\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter\n",
-    "from cyclops.report.utils import flatten_results_dict\n",
-    "from cyclops.tasks import BinaryTabularClassificationTask"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "CyclOps offers a package for documentation of the model through a model report. The `ModelCardReport` class is used to populate and generate the model report as an HTML file. The model report has the following sections:\n",
-    "\n",
-    "- Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).\n",
-    "- Datasets: High level statistics of the training data, including changes in distribution over time.\n",
-    "- Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.\n",
-    "- Fairness Analysis: This section contains the fairness metrics of the model.\n",
-    "- Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.\n",
-    "- Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.\n",
-    "- Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.\n",
-    "\n",
-    "We will use this to document the model development process as we go along and generate the model report at the end.\n",
-    "\n",
-    "`The model report tool is a work in progress and is subject to change.`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "RANDOM_SEED = 85\n",
-    "NAN_THRESHOLD = 0.75\n",
-    "TRAIN_SIZE = 0.8\n",
-    "EVAL_NUM = 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Loading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "diabetes_130_data = fetch_ucirepo(id=296)\n",
-    "features = diabetes_130_data[\"data\"][\"features\"]\n",
-    "targets = diabetes_130_data[\"data\"][\"targets\"]\n",
-    "metadata = diabetes_130_data[\"metadata\"]\n",
-    "variables = diabetes_130_data[\"variables\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "metadata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def transform_label(value):\n",
-    "    \"\"\"Transform string labels of readmission into 0/1 binary labels.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    value: str\n",
-    "        Input value\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    int\n",
-    "        0 if not readmitted or if greater than 30 days, 1 if less than 30 days\n",
-    "\n",
-    "    \"\"\"\n",
-    "    if value in [\"NO\", \">30\"]:\n",
-    "        return 0\n",
-    "    if value == \"<30\":\n",
-    "        return 1\n",
-    "\n",
-    "    raise ValueError(\"Unexpected value for readmission!\")\n",
-    "\n",
-    "\n",
-    "df = features\n",
-    "targets[\"readmitted\"] = targets[\"readmitted\"].apply(transform_label)\n",
-    "df[\"readmitted\"] = targets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Choose a small subset for modelling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df[0:1000]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Remove features that are NaNs or have just a single unique value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "features_to_remove = []\n",
-    "for col in df:\n",
-    "    if len(df[col].value_counts()) <= 1:\n",
-    "        features_to_remove.append(col)\n",
-    "df = df.drop(columns=features_to_remove)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sex values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"gender\")\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    title=\"Gender Distribution\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Gender Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###  Age distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(df, x=\"age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Age Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Outcome distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "df[\"outcome\"] = df[\"readmitted\"].astype(\"int\")\n",
-    "df = df.drop(columns=[\"readmitted\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"outcome\")\n",
-    "fig.update_traces(textinfo=\"percent+label\")\n",
-    "fig.update_layout(title_text=\"Outcome Distribution\")\n",
-    "fig.update_traces(\n",
-    "    hovertemplate=\"Outcome: %{label}<br>Count: \\\n",
-    "    %{value}<br>Percent: %{percent}\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Outcome Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "class_counts = df[\"outcome\"].value_counts()\n",
-    "class_ratio = class_counts[0] / class_counts[1]\n",
-    "print(class_ratio, class_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From the features in the dataset, we select all of them to train the model!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "features_list = list(df.columns)\n",
-    "features_list.remove(\"outcome\")\n",
-    "features_list = sorted(features_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Identifying feature types\n",
-    "\n",
-    "Cyclops `TabularFeatures` class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "tab_features = TabularFeatures(\n",
-    "    data=df.reset_index(),\n",
-    "    features=features_list,\n",
-    "    by=\"index\",\n",
-    "    targets=\"outcome\",\n",
-    ")\n",
-    "print(tab_features.types)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating data preprocessors\n",
-    "\n",
-    "We create a data preprocessor using sklearn's ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"mean\")), (\"scaler\", MinMaxScaler())],\n",
-    ")\n",
-    "\n",
-    "binary_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"most_frequent\"))],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_features = sorted((tab_features.features_by_type(\"numeric\")))\n",
-    "numeric_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in numeric_features\n",
-    "]\n",
-    "print(numeric_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "binary_features = sorted(tab_features.features_by_type(\"binary\"))\n",
-    "binary_features.remove(\"outcome\")\n",
-    "ordinal_features = sorted(\n",
-    "    tab_features.features_by_type(\"ordinal\")\n",
-    "    + [\"medical_specialty\", \"diag_1\", \"diag_2\", \"diag_3\"]\n",
-    ")\n",
-    "binary_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in binary_features\n",
-    "]\n",
-    "ordinal_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in ordinal_features\n",
-    "]\n",
-    "print(binary_features, ordinal_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "preprocessor = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        (\"num\", numeric_transformer, numeric_indices),\n",
-    "        (\n",
-    "            \"onehot\",\n",
-    "            OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False),\n",
-    "            binary_indices + ordinal_indices,\n",
-    "        ),\n",
-    "    ],\n",
-    "    remainder=\"passthrough\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's document the dataset in the model card. This can be done using the `log_dataset` method, which takes the following arguments:\n",
-    "- description: A description of the dataset.\n",
-    "- citation: The citation for the dataset.\n",
-    "- link: A link to a resource for the dataset.\n",
-    "- license_id: The SPDX license identifier for the dataset.\n",
-    "- version: The version of the dataset.\n",
-    "- features: A list of features in the dataset.\n",
-    "- split: The split of the dataset (train, test, validation, etc.).\n",
-    "- sensitive_features: A list of sensitive features used to train/evaluate the model.\n",
-    "- sensitive_feature_justification: A justification for the sensitive features used to train/evaluate the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_dataset(\n",
-    "    description=metadata[\"abstract\"],\n",
-    "    citation=inspect.cleandoc(\n",
-    "        \"\"\"\n",
-    "        @article{strack2014impact,\n",
-    "          title={Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records},\n",
-    "          author={Strack, Beata and DeShazo, Jonathan P and Gennings, Chris and Olmo, Juan L and Ventura, Sebastian and Cios, Krzysztof J and Clore, John N and others},\n",
-    "          journal={BioMed research international},\n",
-    "          volume={2014},\n",
-    "          year={2014},\n",
-    "          publisher={Hindawi}\n",
-    "        }\n",
-    "    \"\"\",\n",
-    "    ),\n",
-    "    link=metadata[\"repository_url\"],\n",
-    "    license_id=\"CC0-1.0\",\n",
-    "    version=\"Version 1\",\n",
-    "    features=features_list,\n",
-    "    sensitive_features=[\"gender\", \"age\", \"race\"],\n",
-    "    sensitive_feature_justification=\"Demographic information like age and gender \\\n",
-    "        often have a strong correlation with health outcomes. For example, older \\\n",
-    "        patients are more likely to have a higher risk of readmission.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating Hugging Face Dataset\n",
-    "\n",
-    "We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = Dataset.from_pandas(df)\n",
-    "dataset.cleanup_cache_files()\n",
-    "print(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = dataset.cast_column(\"outcome\", ClassLabel(num_classes=2))\n",
-    "dataset = dataset.train_test_split(\n",
-    "    train_size=TRAIN_SIZE,\n",
-    "    stratify_by_column=\"outcome\",\n",
-    "    seed=RANDOM_SEED,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model Creation\n",
-    "\n",
-    "CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a SGD classifier to fit a logisitic regression model. The model configurations can be passed to `create_model` based on the sklearn parameters for SGDClassifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_name = \"xgb_classifier\"\n",
-    "model = create_model(model_name, random_state=123)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Task Creation\n",
-    "\n",
-    "We use Cyclops tasks to define our model's task (in this case, readmission prediction), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "readmission_prediction_task = BinaryTabularClassificationTask(\n",
-    "    {model_name: model},\n",
-    "    task_features=features_list,\n",
-    "    task_target=\"outcome\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "readmission_prediction_task.list_models()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training\n",
-    "\n",
-    "If `best_model_params` is passed to the `train` method, the best model will be selected after the hyperparameter search. The parameters in `best_model_params` indicate the values to create the parameters grid.\n",
-    "\n",
-    "Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "best_model_params = {\n",
-    "    \"n_estimators\": [100, 250, 500],\n",
-    "    \"learning_rate\": [0.1, 0.01],\n",
-    "    \"max_depth\": [2, 5],\n",
-    "    \"reg_lambda\": [0, 1, 10],\n",
-    "    \"colsample_bytree\": [0.7, 0.8, 1],\n",
-    "    \"gamma\": [0, 1, 2, 10],\n",
-    "    \"method\": \"random\",\n",
-    "    \"scale_pos_weight\": [int(class_ratio)],\n",
-    "}\n",
-    "readmission_prediction_task.train(\n",
-    "    dataset[\"train\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    best_model_params=best_model_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_params = readmission_prediction_task.list_models_params()[model_name]\n",
-    "print(model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the model parameters to the report.**\n",
-    "\n",
-    "We can add model parameters to the model card using the `log_model_parameters` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_model_parameters(params=model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prediction\n",
-    "\n",
-    "The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "y_pred = readmission_prediction_task.predict(\n",
-    "    dataset[\"test\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    proba=True,\n",
-    "    only_predictions=True,\n",
-    ")\n",
-    "prediction_df = pd.DataFrame(\n",
-    "    {\n",
-    "        \"y_prob\": [y_pred_i[1] for y_pred_i in y_pred],\n",
-    "        \"y_true\": dataset[\"test\"][\"outcome\"],\n",
-    "        \"gender\": dataset[\"test\"][\"gender\"],\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluation\n",
-    "\n",
-    "Evaluation is done using various evaluation metrics that provide different perspectives on the model's predictive abilities i.e. standard performance metrics and fairness metrics.\n",
-    "\n",
-    "The standard performance metrics can be created using the `MetricDict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_auroc\",\n",
-    "    \"binary_average_precision\",\n",
-    "    \"binary_roc_curve\",\n",
-    "    \"binary_precision_recall_curve\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using `SliceSpec` objects. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"value\": \"[50-60)\",\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"value\": \"[60-70)\",\n",
-    "        },\n",
-    "    },\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `MetricDict` can also be defined for the fairness metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "specificity = create_metric(metric_name=\"binary_specificity\", experimental=True)\n",
-    "sensitivity = create_metric(metric_name=\"binary_sensitivity\", experimental=True)\n",
-    "\n",
-    "fpr = -specificity + 1\n",
-    "fnr = -sensitivity + 1\n",
-    "\n",
-    "ber = (fpr + fnr) / 2\n",
-    "\n",
-    "fairness_metric_collection = MetricDict(\n",
-    "    {\n",
-    "        \"Sensitivity\": sensitivity,\n",
-    "        \"Specificity\": specificity,\n",
-    "        \"BER\": ber,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The FairnessConfig helps in setting up and evaluating the fairness of the model predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fairness_config = FairnessConfig(\n",
-    "    metrics=fairness_metric_collection,\n",
-    "    dataset=None,  # dataset is passed from the evaluator\n",
-    "    target_columns=None,  # target columns are passed from the evaluator\n",
-    "    groups=[\"age\"],\n",
-    "    group_base_values={\"age\": \"[40-50)\"},\n",
-    "    thresholds=[0.5],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "results, dataset_with_preds = readmission_prediction_task.evaluate(\n",
-    "    dataset=dataset[\"test\"],\n",
-    "    metrics=metric_collection,\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=slice_spec,\n",
-    "    batch_size=-1,\n",
-    "    fairness_config=fairness_config,\n",
-    "    override_fairness_metrics=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_female, _ = readmission_prediction_task.evaluate(\n",
-    "    dataset=dataset[\"test\"],\n",
-    "    metrics=MetricDict(\n",
-    "        {\n",
-    "            \"BinaryAccuracy\": create_metric(\n",
-    "                metric_name=\"binary_accuracy\",\n",
-    "                experimental=True,\n",
-    "            ),\n",
-    "        },\n",
-    "    ),\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=SliceSpec([{\"gender\": {\"value\": \"Female\"}}], include_overall=False),\n",
-    "    batch_size=-1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the performance metrics to the report.**\n",
-    "\n",
-    "We can add a performance metric to the model card using the `log_performance_metric` method, which expects a dictionary where the keys are in the following format: `slice_name/metric_name`. For instance, `overall/accuracy`. \n",
-    "\n",
-    "We first need to process the evaluation results to get the metrics in the right format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_name = f\"model_for_preds.{model_name}\"\n",
-    "results_flat = flatten_results_dict(\n",
-    "    results=results,\n",
-    "    remove_metrics=[\"BinaryROC\", \"BinaryPrecisionRecallCurve\"],\n",
-    "    model_name=model_name,\n",
-    ")\n",
-    "results_female_flat = flatten_results_dict(\n",
-    "    results=results_female,\n",
-    "    model_name=model_name,\n",
-    ")\n",
-    "# ruff: noqa: W505\n",
-    "for name, metric in results_female_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )\n",
-    "\n",
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the `ClassificationPlotter` to plot the performance metrics and the add the figure to the model card using the `log_plotly_figure` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the ROC curves and AUROC results for all the slices\n",
-    "roc_curves = {\n",
-    "    slice_name: slice_results[\"BinaryROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "aurocs = {\n",
-    "    slice_name: slice_results[\"BinaryAUROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "roc_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the ROC curves for all the slices\n",
-    "roc_plot = plotter.roc_curve_comparison(roc_curves, aurocs=aurocs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=roc_plot,\n",
-    "    caption=\"ROC Curve for Female Patients\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "roc_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the precision-recall curves and average precision results for all the slices\n",
-    "pr_curves = {\n",
-    "    slice_name: slice_results[\"BinaryPrecisionRecallCurve\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "average_precisions = {\n",
-    "    slice_name: slice_results[\"BinaryAveragePrecision\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "pr_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the precision-recall curves for all the slices\n",
-    "pr_plot = plotter.precision_recall_curve_comparison(\n",
-    "    pr_curves,\n",
-    "    auprcs=average_precisions,\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=pr_plot,\n",
-    "    caption=\"Precision-Recall Curve Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "pr_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the overall classification metric values.\n",
-    "overall_performance = {\n",
-    "    metric_name: metric_value\n",
-    "    for metric_name, metric_value in results[model_name][\"overall\"].items()\n",
-    "    if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the overall classification metric values.\n",
-    "overall_performance_plot = plotter.metrics_value(\n",
-    "    overall_performance,\n",
-    "    title=\"Overall Performance\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=overall_performance_plot,\n",
-    "    caption=\"Overall Performance\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "overall_performance_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=slice_metrics_plot,\n",
-    "    caption=\"Slice Metric Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "# ROC curve components\n",
-    "pred_probs = np.array(dataset_with_preds[\"preds.xgb_classifier\"])\n",
-    "true_labels = np.array(dataset_with_preds[\"outcome\"])\n",
-    "roc_curve = binary_roc(true_labels, pred_probs)\n",
-    "ppv = np.zeros_like(roc_curve.thresholds)\n",
-    "npv = np.zeros_like(roc_curve.thresholds)\n",
-    "\n",
-    "# Calculate PPV and NPV for each threshold\n",
-    "for i, threshold in enumerate(roc_curve.thresholds):\n",
-    "    # Calculate PPV and NPV\n",
-    "    ppv[i] = binary_ppv(true_labels, pred_probs, threshold=threshold)\n",
-    "    npv[i] = binary_npv(true_labels, pred_probs, threshold=threshold)\n",
-    "runway_plot = plotter.threshperf(roc_curve, ppv, npv, pred_probs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=runway_plot,\n",
-    "    caption=\"Threshold-Performance plot\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "runway_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot the calibration curve of the model on the test set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "calibration_plot = plotter.calibration(\n",
-    "    prediction_df, y_true_col=\"y_true\", y_prob_col=\"y_prob\", group_col=\"gender\"\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=calibration_plot,\n",
-    "    caption=\"Calibration plot\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "calibration_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reformatting the fairness metrics\n",
-    "fairness_results = copy.deepcopy(results[\"fairness\"])\n",
-    "fairness_metrics = {}\n",
-    "# remove the group size from the fairness results and add it to the slice name\n",
-    "for slice_name, slice_results in fairness_results.items():\n",
-    "    group_size = slice_results.pop(\"Group Size\")\n",
-    "    fairness_metrics[f\"{slice_name} (Size={group_size})\"] = slice_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the fairness metrics\n",
-    "fairness_plot = plotter.metrics_comparison_scatter(\n",
-    "    fairness_metrics,\n",
-    "    title=\"Fairness Metrics\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fairness_plot,\n",
-    "    caption=\"Fairness Metrics\",\n",
-    "    section_name=\"fairness analysis\",\n",
-    ")\n",
-    "fairness_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Report Generation\n",
-    "\n",
-    "Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.\n",
-    "\n",
-    "\n",
-    "Let's start with populating the model details section, which includes the following fields by default:\n",
-    "- description: A high-level description of the model and its usage for a general audience.\n",
-    "- version: The version of the model.\n",
-    "- owners: The individuals or organizations that own the model.\n",
-    "- license: The license under which the model is made available.\n",
-    "- citation: The citation for the model.\n",
-    "- references: Links to resources that are relevant to the model.\n",
-    "- path: The path to where the model is stored.\n",
-    "- regulatory_requirements: The regulatory requirements that are relevant to the model.\n",
-    "\n",
-    "We can add additional fields to the model details section by passing a dictionary to the `log_from_dict` method and specifying the section name as `model_details`. You can also use the `log_descriptor` method to add a new field object with a `description` attribute to any section of the model card."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"Readmission Prediction Model\",\n",
-    "        \"description\": \"The model was trained on the Diabetes 130-US Hospitals for Years 1999-2008 \\\n",
-    "        dataset to predict risk of readmission within 30 days of discharge.\",\n",
-    "    },\n",
-    "    section_name=\"model_details\",\n",
-    ")\n",
-    "\n",
-    "report.log_version(\n",
-    "    version_str=\"0.0.1\",\n",
-    "    date=str(date.today()),\n",
-    "    description=\"Initial Release\",\n",
-    ")\n",
-    "report.log_owner(\n",
-    "    name=\"CyclOps Team\",\n",
-    "    contact=\"vectorinstitute.github.io/cyclops/\",\n",
-    "    email=\"cyclops@vectorinstitute.ai\",\n",
-    ")\n",
-    "report.log_license(identifier=\"Apache-2.0\")\n",
-    "report.log_reference(\n",
-    "    link=\"https://xgboost.readthedocs.io/en/stable/python/python_api.html\",  # noqa: E501\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, let's populate the considerations section, which includes the following fields by default:\n",
-    "- users: The intended users of the model.\n",
-    "- use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases.\n",
-    "- fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms.\n",
-    "- ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This can be populated using the  `log_risk` method.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"users\": [\n",
-    "            {\"description\": \"Hospitals\"},\n",
-    "            {\"description\": \"Clinicians\"},\n",
-    "        ],\n",
-    "    },\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_user(description=\"ML Engineers\")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of readmission.\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of pathologies and conditions other\\\n",
-    "    than risk of readmission.\",\n",
-    "    kind=\"out-of-scope\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"sex, age\",\n",
-    "    benefit=\"Improved health outcomes for patients.\",\n",
-    "    harm=\"Biased predictions for patients in certain groups (e.g. older patients) \\\n",
-    "        may lead to worse health outcomes.\",\n",
-    "    mitigation_strategy=\"We will monitor the performance of the model on these groups \\\n",
-    "        and retrain the model if the performance drops below a certain threshold.\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"The model may be used to make decisions that affect the health of patients.\",\n",
-    "    mitigation_strategy=\"The model should be continuously monitored for performance \\\n",
-    "        and retrained if the performance drops below a certain threshold.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the model card is populated, you can generate the report using the `export` method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named `cyclops_reports` in the current working directory. You can change the path by passing a `output_dir` argument when instantiating the `ModelCardReport` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "synthetic_timestamps = pd.date_range(\n",
-    "    start=\"20/6/2024\", periods=3, freq=\"W\"\n",
-    ").values.astype(str)\n",
-    "report_path = report.export(\n",
-    "    output_filename=\"readmission_report_periodic.html\",\n",
-    "    synthetic_timestamp=synthetic_timestamps[EVAL_NUM - 1],\n",
-    "    last_n_evals=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "You can view the generated HTML [report](./heart_failure_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt b/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt
deleted file mode 100644
index 1f987623f..000000000
--- a/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt
+++ /dev/null
@@ -1,1398 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Heart Failure Prediction\n",
-    "\n",
-    "This notebook showcases heart failure prediction on a [popular Kaggle dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) using CyclOps. The task is formulated as a binary classification task, where we predict the probability that a patient will have heart failure."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"Heart failure prediction.\"\"\"\n",
-    "\n",
-    "import copy\n",
-    "import inspect\n",
-    "import shutil\n",
-    "from datetime import date\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "from datasets import Dataset\n",
-    "from datasets.features import ClassLabel\n",
-    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
-    "\n",
-    "from cyclops.data.df.feature import TabularFeatures\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate.fairness import FairnessConfig  # noqa: E402\n",
-    "from cyclops.evaluate.metrics import create_metric\n",
-    "from cyclops.evaluate.metrics.experimental.functional import (\n",
-    "    binary_npv,\n",
-    "    binary_ppv,\n",
-    "    binary_roc,\n",
-    ")\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.models.catalog import create_model\n",
-    "from cyclops.report import ModelCardReport\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter\n",
-    "from cyclops.report.utils import flatten_results_dict\n",
-    "from cyclops.tasks import BinaryTabularClassificationTask\n",
-    "from cyclops.utils.file import join, load_dataframe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "CyclOps offers a package for documentation of the model through a model report. The `ModelCardReport` class is used to populate and generate the model report as an HTML file. The model report has the following sections:\n",
-    "\n",
-    "- Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).\n",
-    "- Datasets: High level statistics of the training data, including changes in distribution over time.\n",
-    "- Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.\n",
-    "- Fairness Analysis: This section contains the fairness metrics of the model.\n",
-    "- Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.\n",
-    "- Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.\n",
-    "- Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.\n",
-    "\n",
-    "We will use this to document the model development process as we go along and generate the model report at the end.\n",
-    "\n",
-    "`The model report tool is a work in progress and is subject to change.`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "DATA_DIR = \"./data\"\n",
-    "RANDOM_SEED = 85\n",
-    "NAN_THRESHOLD = 0.75\n",
-    "TRAIN_SIZE = 0.8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Loading\n",
-    "\n",
-    "Before starting, make sure to install the Kaggle API by running `pip install kaggle`. To use the Kaggle API, you need to sign up for a Kaggle account at https://www.kaggle.com. Then go to the 'Account' tab of your user profile (`https://www.kaggle.com/<username>/account`) and select 'Create API Token'. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location `~/.kaggle/kaggle.json` on your machine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "api = KaggleApi()\n",
-    "api.authenticate()\n",
-    "api.dataset_download_files(\n",
-    "    \"fedesoriano/heart-failure-prediction\",\n",
-    "    path=DATA_DIR,\n",
-    "    unzip=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "df = load_dataframe(join(DATA_DIR, \"heart.csv\"), file_format=\"csv\")\n",
-    "df = df.reset_index()\n",
-    "df[\"ID\"] = df.index\n",
-    "print(df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sex values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"Sex\")\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    title=\"Sex Distribution\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Sex Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###  Age distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(df, x=\"Age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Age Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Outcome distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "df[\"outcome\"] = df[\"HeartDisease\"].astype(\"int\")\n",
-    "df = df.drop(columns=[\"HeartDisease\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"outcome\")\n",
-    "fig.update_traces(textinfo=\"percent+label\")\n",
-    "fig.update_layout(title_text=\"Outcome Distribution\")\n",
-    "fig.update_traces(\n",
-    "    hovertemplate=\"Outcome: %{label}<br>Count: \\\n",
-    "    %{value}<br>Percent: %{percent}\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Outcome Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "class_counts = df[\"outcome\"].value_counts()\n",
-    "class_ratio = class_counts[0] / class_counts[1]\n",
-    "print(class_ratio, class_counts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "print(df.columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From all the features in the dataset, we select 20 of them which was reported by [Li et al.](https://pubmed.ncbi.nlm.nih.gov/34301649/)  to be the most important features in this classification task. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "features_list = [\n",
-    "    \"Age\",\n",
-    "    \"Sex\",\n",
-    "    \"ChestPainType\",\n",
-    "    \"RestingBP\",\n",
-    "    \"Cholesterol\",\n",
-    "    \"FastingBS\",\n",
-    "    \"RestingECG\",\n",
-    "    \"MaxHR\",\n",
-    "    \"ExerciseAngina\",\n",
-    "    \"Oldpeak\",\n",
-    "    \"ST_Slope\",\n",
-    "]\n",
-    "\n",
-    "features_list = sorted(features_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Identifying feature types\n",
-    "\n",
-    "Cyclops `TabularFeatures` class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "tab_features = TabularFeatures(\n",
-    "    data=df.reset_index(),\n",
-    "    features=features_list,\n",
-    "    by=\"ID\",\n",
-    "    targets=\"outcome\",\n",
-    ")\n",
-    "print(tab_features.types)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating data preprocessors\n",
-    "\n",
-    "We create a data preprocessor using sklearn's ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"mean\")), (\"scaler\", MinMaxScaler())],\n",
-    ")\n",
-    "\n",
-    "binary_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"most_frequent\"))],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_features = sorted((tab_features.features_by_type(\"numeric\")))\n",
-    "numeric_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in numeric_features\n",
-    "]\n",
-    "print(numeric_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "binary_features = sorted(tab_features.features_by_type(\"binary\"))\n",
-    "binary_features.remove(\"outcome\")\n",
-    "ordinal_features = sorted(tab_features.features_by_type(\"ordinal\"))\n",
-    "binary_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in binary_features\n",
-    "]\n",
-    "ordinal_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in ordinal_features\n",
-    "]\n",
-    "print(binary_features, ordinal_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "preprocessor = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        (\"num\", numeric_transformer, numeric_indices),\n",
-    "        (\n",
-    "            \"onehot\",\n",
-    "            OneHotEncoder(handle_unknown=\"ignore\"),\n",
-    "            binary_indices + ordinal_indices,\n",
-    "        ),\n",
-    "    ],\n",
-    "    remainder=\"passthrough\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's document the dataset in the model card. This can be done using the `log_dataset` method, which takes the following arguments:\n",
-    "- description: A description of the dataset.\n",
-    "- citation: The citation for the dataset.\n",
-    "- link: A link to a resource for the dataset.\n",
-    "- license_id: The SPDX license identifier for the dataset.\n",
-    "- version: The version of the dataset.\n",
-    "- features: A list of features in the dataset.\n",
-    "- split: The split of the dataset (train, test, validation, etc.).\n",
-    "- sensitive_features: A list of sensitive features used to train/evaluate the model.\n",
-    "- sensitive_feature_justification: A justification for the sensitive features used to train/evaluate the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_dataset(\n",
-    "    description=\"\"\"This dataset was created by combining different datasets\n",
-    "    already available independently but not combined before. In this dataset,\n",
-    "    5 heart datasets are combined over 11 common features. Every dataset used\n",
-    "    can be found under the Index of heart disease datasets from UCI\n",
-    "    Machine Learning Repository on the following link:\n",
-    "    https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/.\"\"\",\n",
-    "    citation=inspect.cleandoc(\n",
-    "        \"\"\"\n",
-    "        @misc{fedesoriano,\n",
-    "          title={Heart Failure Prediction Dataset.},\n",
-    "          author={Fedesoriano, F},\n",
-    "          year={2021},\n",
-    "          publisher={Kaggle}\n",
-    "        }\n",
-    "    \"\"\",\n",
-    "    ),\n",
-    "    link=\"\"\"\n",
-    "    https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction\n",
-    "    \"\"\",\n",
-    "    license_id=\"CC0-1.0\",\n",
-    "    version=\"Version 1\",\n",
-    "    features=features_list,\n",
-    "    sensitive_features=[\"Sex\", \"Age\"],\n",
-    "    sensitive_feature_justification=\"Demographic information like age and gender \\\n",
-    "        often have a strong correlation with health outcomes. For example, older \\\n",
-    "        patients are more likely to have a higher risk of heart disease.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating Hugging Face Dataset\n",
-    "\n",
-    "We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = Dataset.from_pandas(df)\n",
-    "dataset.cleanup_cache_files()\n",
-    "print(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = dataset.cast_column(\"outcome\", ClassLabel(num_classes=2))\n",
-    "dataset = dataset.train_test_split(\n",
-    "    train_size=TRAIN_SIZE,\n",
-    "    stratify_by_column=\"outcome\",\n",
-    "    seed=RANDOM_SEED,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model Creation\n",
-    "\n",
-    "CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a SGD classifier to fit a logisitic regression model. The model configurations can be passed to `create_model` based on the sklearn parameters for SGDClassifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_name = \"sgd_classifier\"\n",
-    "model = create_model(model_name, random_state=123, verbose=0, class_weight=\"balanced\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Task Creation\n",
-    "\n",
-    "We use Cyclops tasks to define our model's task (in this case, heart failure prediction), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "heart_failure_prediction_task = BinaryTabularClassificationTask(\n",
-    "    {model_name: model},\n",
-    "    task_features=features_list,\n",
-    "    task_target=\"outcome\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "heart_failure_prediction_task.list_models()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training\n",
-    "\n",
-    "If `best_model_params` is passed to the `train` method, the best model will be selected after the hyperparameter search. The parameters in `best_model_params` indicate the values to create the parameters grid.\n",
-    "\n",
-    "Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "best_model_params = {\n",
-    "    \"alpha\": [0.0001, 0.001, 0.01, 0.1, 1, 10, 100],\n",
-    "    \"learning_rate\": [\"constant\", \"optimal\", \"invscaling\", \"adaptive\"],\n",
-    "    \"eta0\": [0.1, 0.01, 0.001, 0.0001],\n",
-    "    \"metric\": \"roc_auc\",\n",
-    "    \"method\": \"grid\",\n",
-    "}\n",
-    "\n",
-    "heart_failure_prediction_task.train(\n",
-    "    dataset[\"train\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    best_model_params=best_model_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_params = heart_failure_prediction_task.list_models_params()[model_name]\n",
-    "print(model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the model parameters to the report.**\n",
-    "\n",
-    "We can add model parameters to the model card using the `log_model_parameters` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_model_parameters(params=model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prediction\n",
-    "\n",
-    "The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "y_pred = heart_failure_prediction_task.predict(\n",
-    "    dataset[\"test\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    proba=True,\n",
-    "    only_predictions=True,\n",
-    ")\n",
-    "prediction_df = pd.DataFrame(\n",
-    "    {\n",
-    "        \"y_prob\": [y_pred_i[1] for y_pred_i in y_pred],\n",
-    "        \"y_true\": dataset[\"test\"][\"outcome\"],\n",
-    "        \"Sex\": dataset[\"test\"][\"Sex\"],\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluation\n",
-    "\n",
-    "Evaluation is done using various evaluation metrics that provide different perspectives on the model's predictive abilities i.e. standard performance metrics and fairness metrics.\n",
-    "\n",
-    "The standard performance metrics can be created using the `MetricDict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_auroc\",\n",
-    "    \"binary_average_precision\",\n",
-    "    \"binary_roc_curve\",\n",
-    "    \"binary_precision_recall_curve\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using `SliceSpec` objects. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"Age\": {\n",
-    "            \"min_value\": 30,\n",
-    "            \"max_value\": 50,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"Age\": {\n",
-    "            \"min_value\": 50,\n",
-    "            \"max_value\": 70,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\"Sex\": {\"value\": \"M\"}},\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `MetricDict` can also be defined for the fairness metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "specificity = create_metric(metric_name=\"binary_specificity\", experimental=True)\n",
-    "sensitivity = create_metric(metric_name=\"binary_sensitivity\", experimental=True)\n",
-    "\n",
-    "fpr = -specificity + 1\n",
-    "fnr = -sensitivity + 1\n",
-    "\n",
-    "ber = (fpr + fnr) / 2\n",
-    "\n",
-    "fairness_metric_collection = MetricDict(\n",
-    "    {\n",
-    "        \"Sensitivity\": sensitivity,\n",
-    "        \"Specificity\": specificity,\n",
-    "        \"BER\": ber,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The FairnessConfig helps in setting up and evaluating the fairness of the model predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fairness_config = FairnessConfig(\n",
-    "    metrics=fairness_metric_collection,\n",
-    "    dataset=None,  # dataset is passed from the evaluator\n",
-    "    target_columns=None,  # target columns are passed from the evaluator\n",
-    "    groups=[\"Age\"],\n",
-    "    group_bins={\"Age\": [40, 50]},\n",
-    "    group_base_values={\"Age\": 40},\n",
-    "    thresholds=[0.5],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "results, dataset_with_preds = heart_failure_prediction_task.evaluate(\n",
-    "    dataset=dataset[\"test\"],\n",
-    "    metrics=metric_collection,\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=slice_spec,\n",
-    "    batch_size=-1,\n",
-    "    fairness_config=fairness_config,\n",
-    "    override_fairness_metrics=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_female, _ = heart_failure_prediction_task.evaluate(\n",
-    "    dataset=dataset[\"test\"],\n",
-    "    metrics=MetricDict(\n",
-    "        {\n",
-    "            \"BinaryAccuracy\": create_metric(\n",
-    "                metric_name=\"binary_accuracy\",\n",
-    "                experimental=True,\n",
-    "            ),\n",
-    "        },\n",
-    "    ),\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=SliceSpec(\n",
-    "        [{\"Sex\": {\"value\": \"F\"}}, {\"Sex\": {\"value\": \"M\"}}], include_overall=False\n",
-    "    ),\n",
-    "    batch_size=-1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the performance metrics to the report.**\n",
-    "\n",
-    "We can add a performance metric to the model card using the `log_performance_metric` method, which expects a dictionary where the keys are in the following format: `slice_name/metric_name`. For instance, `overall/accuracy`. \n",
-    "\n",
-    "We first need to process the evaluation results to get the metrics in the right format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_name = f\"model_for_preds.{model_name}\"\n",
-    "results_flat = flatten_results_dict(\n",
-    "    results=results,\n",
-    "    remove_metrics=[\"BinaryROC\", \"BinaryPrecisionRecallCurve\"],\n",
-    "    model_name=model_name,\n",
-    ")\n",
-    "results_female_flat = flatten_results_dict(\n",
-    "    results=results_female,\n",
-    "    model_name=model_name,\n",
-    ")\n",
-    "# ruff: noqa: W505\n",
-    "for name, metric in results_female_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )\n",
-    "\n",
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the `ClassificationPlotter` to plot the performance metrics and the add the figure to the model card using the `log_plotly_figure` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the ROC curves and AUROC results for all the slices\n",
-    "roc_curves = {\n",
-    "    slice_name: slice_results[\"BinaryROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "aurocs = {\n",
-    "    slice_name: slice_results[\"BinaryAUROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "roc_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the ROC curves for all the slices\n",
-    "roc_plot = plotter.roc_curve_comparison(roc_curves, aurocs=aurocs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=roc_plot,\n",
-    "    caption=\"ROC Curve for Female Patients\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "roc_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the precision-recall curves and average precision results for all the slices\n",
-    "pr_curves = {\n",
-    "    slice_name: slice_results[\"BinaryPrecisionRecallCurve\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "average_precisions = {\n",
-    "    slice_name: slice_results[\"BinaryAveragePrecision\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "pr_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the precision-recall curves for all the slices\n",
-    "pr_plot = plotter.precision_recall_curve_comparison(\n",
-    "    pr_curves,\n",
-    "    auprcs=average_precisions,\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=pr_plot,\n",
-    "    caption=\"Precision-Recall Curve Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "pr_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the overall classification metric values.\n",
-    "overall_performance = {\n",
-    "    metric_name: metric_value\n",
-    "    for metric_name, metric_value in results[model_name][\"overall\"].items()\n",
-    "    if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the overall classification metric values.\n",
-    "overall_performance_plot = plotter.metrics_value(\n",
-    "    overall_performance,\n",
-    "    title=\"Overall Performance\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=overall_performance_plot,\n",
-    "    caption=\"Overall Performance\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "overall_performance_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=slice_metrics_plot,\n",
-    "    caption=\"Slice Metric Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "# ROC curve components\n",
-    "pred_probs = np.array(dataset_with_preds[\"preds.sgd_classifier\"])\n",
-    "true_labels = np.array(dataset_with_preds[\"outcome\"])\n",
-    "roc_curve = binary_roc(true_labels, pred_probs)\n",
-    "ppv = np.zeros_like(roc_curve.thresholds)\n",
-    "npv = np.zeros_like(roc_curve.thresholds)\n",
-    "\n",
-    "# Calculate PPV and NPV for each threshold\n",
-    "for i, threshold in enumerate(roc_curve.thresholds):\n",
-    "    # Calculate PPV and NPV\n",
-    "    ppv[i] = binary_ppv(true_labels, pred_probs, threshold=threshold)\n",
-    "    npv[i] = binary_npv(true_labels, pred_probs, threshold=threshold)\n",
-    "runway_plot = plotter.threshperf(roc_curve, ppv, npv, pred_probs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=runway_plot,\n",
-    "    caption=\"Threshold-Performance plot\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "runway_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot the calibration curve of the model on the test set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "calibration_plot = plotter.calibration(\n",
-    "    prediction_df, y_true_col=\"y_true\", y_prob_col=\"y_prob\", group_col=\"Sex\"\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=calibration_plot,\n",
-    "    caption=\"Calibration plot\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "calibration_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reformatting the fairness metrics\n",
-    "fairness_results = copy.deepcopy(results[\"fairness\"])\n",
-    "fairness_metrics = {}\n",
-    "# remove the group size from the fairness results and add it to the slice name\n",
-    "for slice_name, slice_results in fairness_results.items():\n",
-    "    group_size = slice_results.pop(\"Group Size\")\n",
-    "    fairness_metrics[f\"{slice_name} (Size={group_size})\"] = slice_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the fairness metrics\n",
-    "fairness_plot = plotter.metrics_comparison_scatter(\n",
-    "    fairness_metrics,\n",
-    "    title=\"Fairness Metrics\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fairness_plot,\n",
-    "    caption=\"Fairness Metrics\",\n",
-    "    section_name=\"fairness analysis\",\n",
-    ")\n",
-    "fairness_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Report Generation\n",
-    "\n",
-    "Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.\n",
-    "\n",
-    "\n",
-    "Let's start with populating the model details section, which includes the following fields by default:\n",
-    "- description: A high-level description of the model and its usage for a general audience.\n",
-    "- version: The version of the model.\n",
-    "- owners: The individuals or organizations that own the model.\n",
-    "- license: The license under which the model is made available.\n",
-    "- citation: The citation for the model.\n",
-    "- references: Links to resources that are relevant to the model.\n",
-    "- path: The path to where the model is stored.\n",
-    "- regulatory_requirements: The regulatory requirements that are relevant to the model.\n",
-    "\n",
-    "We can add additional fields to the model details section by passing a dictionary to the `log_from_dict` method and specifying the section name as `model_details`. You can also use the `log_descriptor` method to add a new field object with a `description` attribute to any section of the model card."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"Heart Failure Prediction Model\",\n",
-    "        \"description\": \"The model was trained on the Kaggle Heart Failure \\\n",
-    "        Prediction Dataset to predict risk of heart failure.\",\n",
-    "    },\n",
-    "    section_name=\"model_details\",\n",
-    ")\n",
-    "\n",
-    "report.log_version(\n",
-    "    version_str=\"0.0.1\",\n",
-    "    date=str(date.today()),\n",
-    "    description=\"Initial Release\",\n",
-    ")\n",
-    "report.log_owner(\n",
-    "    name=\"CyclOps Team\",\n",
-    "    contact=\"vectorinstitute.github.io/cyclops/\",\n",
-    "    email=\"cyclops@vectorinstitute.ai\",\n",
-    ")\n",
-    "report.log_license(identifier=\"Apache-2.0\")\n",
-    "report.log_reference(\n",
-    "    link=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\",  # noqa: E501\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, let's populate the considerations section, which includes the following fields by default:\n",
-    "- users: The intended users of the model.\n",
-    "- use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases.\n",
-    "- fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms.\n",
-    "- ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This can be populated using the  `log_risk` method.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"users\": [\n",
-    "            {\"description\": \"Hospitals\"},\n",
-    "            {\"description\": \"Clinicians\"},\n",
-    "        ],\n",
-    "    },\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_user(description=\"ML Engineers\")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of heart failure.\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of pathologies and conditions other\\\n",
-    "    than heart failure.\",\n",
-    "    kind=\"out-of-scope\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"sex, age\",\n",
-    "    benefit=\"Improved health outcomes for patients.\",\n",
-    "    harm=\"Biased predictions for patients in certain groups (e.g. older patients) \\\n",
-    "        may lead to worse health outcomes.\",\n",
-    "    mitigation_strategy=\"We will monitor the performance of the model on these groups \\\n",
-    "        and retrain the model if the performance drops below a certain threshold.\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"The model may be used to make decisions that affect the health of patients.\",\n",
-    "    mitigation_strategy=\"The model should be continuously monitored for performance \\\n",
-    "        and retrained if the performance drops below a certain threshold.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the model card is populated, you can generate the report using the `export` method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named `cyclops_reports` in the current working directory. You can change the path by passing a `output_dir` argument when instantiating the `ModelCardReport` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "np.random.seed(42)\n",
-    "\n",
-    "synthetic_timestamps = pd.date_range(\n",
-    "    start=\"1/1/2020\", periods=10, freq=\"D\"\n",
-    ").values.astype(str)\n",
-    "\n",
-    "\n",
-    "report._model_card.overview = None\n",
-    "report_path = report.export(\n",
-    "    output_filename=\"heart_failure_report_periodic.html\",\n",
-    "    synthetic_timestamp=synthetic_timestamps[0],\n",
-    "    last_n_evals=3,\n",
-    ")\n",
-    "\n",
-    "shutil.copy(f\"{report_path}\", \".\")\n",
-    "metric_save = None\n",
-    "for i in range(len(synthetic_timestamps[1:])):\n",
-    "    if i == 3:\n",
-    "        report._model_card.quantitative_analysis.performance_metrics.append(\n",
-    "            metric_save,\n",
-    "        )\n",
-    "    report._model_card.overview = None\n",
-    "    for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "        metric.value = np.clip(\n",
-    "            metric.value + np.random.normal(0, 0.1),\n",
-    "            0,\n",
-    "            1,\n",
-    "        )\n",
-    "        metric.tests[0].passed = bool(metric.value >= 0.7)\n",
-    "    if i == 2:\n",
-    "        metrics = []\n",
-    "        for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "            if metric.type == \"BinaryAccuracy\" and metric.slice == \"Age:[30 - 50)\":\n",
-    "                metric_save = copy.deepcopy(metric)\n",
-    "            else:\n",
-    "                metrics.append(metric)\n",
-    "        report._model_card.quantitative_analysis.performance_metrics = metrics\n",
-    "    report_path = report.export(\n",
-    "        output_filename=\"heart_failure_report_periodic.html\",\n",
-    "        synthetic_timestamp=synthetic_timestamps[i + 1],\n",
-    "    )\n",
-    "    shutil.copy(f\"{report_path}\", \".\")\n",
-    "shutil.rmtree(\"./cyclops_report\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "You can view the generated HTML [report](./heart_failure_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt b/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt
deleted file mode 100644
index 5ad66b0c6..000000000
--- a/api/_sources/tutorials/mimiciv/mortality_prediction.ipynb.txt
+++ /dev/null
@@ -1,1325 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Mortality Prediction\n",
-    "\n",
-    "This notebook showcases mortality prediction on the [MIMICIV](https://physionet.org/content/mimiciv/2.0) dataset using CyclOps. The task is formulated as a binary classification task, whether the patient will die within the next N days. The prediction can be made after M number of days after admission. For example, if N = 14 and M = 1, we are predicting risk of patient mortality within 14 days of admission after considering 24 hours of data after admission."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"Mortality Prediction.\"\"\"\n",
-    "\n",
-    "import copy\n",
-    "import shutil\n",
-    "from datetime import date\n",
-    "\n",
-    "import cycquery.ops as qo\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "import plotly.graph_objects as go\n",
-    "from cycquery import MIMICIVQuerier\n",
-    "from datasets import Dataset\n",
-    "from datasets.features import ClassLabel\n",
-    "from imblearn.over_sampling import SMOTE\n",
-    "from imblearn.pipeline import Pipeline as ImbPipeline\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
-    "\n",
-    "from cyclops.data.aggregate import RESTRICT_TIMESTAMP, Aggregator\n",
-    "from cyclops.data.clean import normalize_names\n",
-    "from cyclops.data.df.feature import TabularFeatures\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate.fairness import FairnessConfig  # noqa: E402\n",
-    "from cyclops.evaluate.metrics import create_metric\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.models.catalog import create_model\n",
-    "from cyclops.report import ModelCardReport\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter\n",
-    "from cyclops.report.utils import flatten_results_dict\n",
-    "from cyclops.tasks import BinaryTabularClassificationTask\n",
-    "from cyclops.utils.common import add_years_approximate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "CyclOps offers a package for documentation of the model through a model report. The `ModelCardReport` class is used to populate and generate the model report as an HTML file. The model report has the following sections:\n",
-    "\n",
-    "- Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).\n",
-    "- Datasets: High level statistics of the training data, including changes in distribution over time.\n",
-    "- Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.\n",
-    "- Fairness Analysis: This section contains the fairness metrics of the model.\n",
-    "- Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.\n",
-    "- Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.\n",
-    "- Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.\n",
-    "\n",
-    "We will use this to document the model development process as we go along and generate the model report at the end.\n",
-    "\n",
-    "`The model report tool is a work in progress and is subject to change.`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "M = 1\n",
-    "N = 14\n",
-    "NAN_THRESHOLD = 0.25\n",
-    "TRAIN_SIZE = 0.8\n",
-    "RANDOM_SEED = 12"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Querying & Processing\n",
-    "\n",
-    "### Compute mortality (labels)\n",
-    "\n",
-    "1. Get encounters\n",
-    "2. Filter out encounters less than M days\n",
-    "3. Set label = 1 for encounters where deathtime is within N days after admission\n",
-    "4. Get lab events\n",
-    "5. Aggregate them by computing mean, merge with encounter data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "querier = MIMICIVQuerier(\n",
-    "    dbms=\"postgresql\",\n",
-    "    port=5432,\n",
-    "    host=\"localhost\",\n",
-    "    database=\"mimiciv-2.0\",\n",
-    "    user=\"postgres\",\n",
-    "    password=\"pwd\",\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def get_encounters():\n",
-    "    \"\"\"Get encounters data.\"\"\"\n",
-    "    patients = querier.patients()\n",
-    "    encounters = querier.mimiciv_hosp.admissions()\n",
-    "    drop_op = qo.Drop(\n",
-    "        [\"language\", \"marital_status\", \"edregtime\", \"edouttime\"],\n",
-    "    )\n",
-    "    encounters = encounters.ops(drop_op)\n",
-    "    patient_encounters = patients.join(encounters, on=\"subject_id\")\n",
-    "    patient_encounters = patient_encounters.run()\n",
-    "    patient_encounters[\"age\"] = (\n",
-    "        patient_encounters[\"admittime\"].dt.year\n",
-    "        - patient_encounters[\"anchor_year\"]\n",
-    "        + patient_encounters[\"anchor_age\"]\n",
-    "    )\n",
-    "    for col in [\"admittime\", \"dischtime\", \"deathtime\"]:\n",
-    "        patient_encounters[col] = add_years_approximate(\n",
-    "            patient_encounters[col],\n",
-    "            patient_encounters[\"anchor_year_difference\"],\n",
-    "        )\n",
-    "\n",
-    "    return patient_encounters[\n",
-    "        [\n",
-    "            \"hadm_id\",\n",
-    "            \"admittime\",\n",
-    "            \"dischtime\",\n",
-    "            \"deathtime\",\n",
-    "            \"anchor_age\",\n",
-    "            \"age\",\n",
-    "            \"gender\",\n",
-    "            \"anchor_year_difference\",\n",
-    "            \"admission_location\",\n",
-    "            \"admission_type\",\n",
-    "            \"insurance\",\n",
-    "            \"hospital_expire_flag\",\n",
-    "        ]\n",
-    "    ]\n",
-    "\n",
-    "\n",
-    "def compute_mortality_outcome(patient_encounters):\n",
-    "    \"\"\"Compute mortality outcome.\"\"\"\n",
-    "    # Drop encounters ending in death which don't have a death timestamp\n",
-    "    invalid = (patient_encounters[\"hospital_expire_flag\"] == 1) & (\n",
-    "        patient_encounters[\"deathtime\"].isna()\n",
-    "    )\n",
-    "    patient_encounters = patient_encounters[~invalid]\n",
-    "    print(f\"Encounters with death flag but no death timestamp: {invalid.sum()}\")\n",
-    "    # Drop encounters which are shorter than M days\n",
-    "    invalid = (\n",
-    "        patient_encounters[\"dischtime\"] - patient_encounters[\"admittime\"]\n",
-    "    ).dt.days < M\n",
-    "    patient_encounters = patient_encounters[~invalid]\n",
-    "    print(f\"Encounters shorter than {M} days: {invalid.sum()}\")\n",
-    "    # Death timestamp is within (<=) N days of admission\n",
-    "    valid = (\n",
-    "        patient_encounters[\"deathtime\"] - patient_encounters[\"admittime\"]\n",
-    "    ).dt.days <= N\n",
-    "    print(f\"Encounters with death timestamp within {N} days: {valid.sum()}\")\n",
-    "    # (Died in hospital) & (Death timestamp is defined)\n",
-    "    print(len(patient_encounters))\n",
-    "    patient_encounters[\"mortality_outcome\"] = pd.Series(\n",
-    "        [0] * len(patient_encounters),\n",
-    "        index=patient_encounters.index,\n",
-    "        dtype=\"int64[pyarrow]\",\n",
-    "    )\n",
-    "    patient_encounters.loc[valid, \"mortality_outcome\"] = 1\n",
-    "    print(\n",
-    "        f\"Encounters with mortality outcome for the model: {patient_encounters['mortality_outcome'].sum()}\",\n",
-    "    )\n",
-    "\n",
-    "    return patient_encounters\n",
-    "\n",
-    "\n",
-    "def get_labevents(patient_encounters):\n",
-    "    \"\"\"Get labevents data.\"\"\"\n",
-    "    labevents = querier.labevents().run(index_col=\"hadm_id\", batch_mode=True)\n",
-    "\n",
-    "    def process_labevents(labevents, patient_encounters):\n",
-    "        \"\"\"Process labevents before aggregation.\"\"\"\n",
-    "        # Reverse deidentified dating\n",
-    "        labevents = pd.merge(\n",
-    "            patient_encounters[\n",
-    "                [\n",
-    "                    \"hadm_id\",\n",
-    "                    \"anchor_year_difference\",\n",
-    "                ]\n",
-    "            ],\n",
-    "            labevents,\n",
-    "            on=\"hadm_id\",\n",
-    "        )\n",
-    "        labevents[\"charttime\"] = add_years_approximate(\n",
-    "            labevents[\"charttime\"],\n",
-    "            labevents[\"anchor_year_difference\"],\n",
-    "        )\n",
-    "        labevents = labevents.drop(\"anchor_year_difference\", axis=1)\n",
-    "        # Pre-processing\n",
-    "        labevents[\"label\"] = normalize_names(labevents[\"label\"])\n",
-    "        labevents[\"category\"] = normalize_names(labevents[\"category\"])\n",
-    "\n",
-    "        return labevents\n",
-    "\n",
-    "    start_timestamps = (\n",
-    "        patient_encounters[[\"hadm_id\", \"admittime\"]]\n",
-    "        .set_index(\"hadm_id\")\n",
-    "        .rename({\"admittime\": RESTRICT_TIMESTAMP}, axis=1)\n",
-    "    )\n",
-    "    mean_aggregator = Aggregator(\n",
-    "        aggfuncs={\n",
-    "            \"valuenum\": \"mean\",\n",
-    "        },\n",
-    "        window_duration=M * 24,\n",
-    "        window_start_time=start_timestamps,\n",
-    "        timestamp_col=\"charttime\",\n",
-    "        time_by=\"hadm_id\",\n",
-    "        agg_by=[\"hadm_id\", \"label\"],\n",
-    "    )\n",
-    "    means_df = pd.DataFrame()\n",
-    "    for batch_num, labevents_batch in enumerate(labevents):\n",
-    "        labevents_batch = process_labevents(  # noqa: PLW2901\n",
-    "            labevents_batch,\n",
-    "            patient_encounters,\n",
-    "        )\n",
-    "        means = mean_aggregator.fit_transform(\n",
-    "            labevents_batch,\n",
-    "        )\n",
-    "        means = means.reset_index()\n",
-    "        means = means.pivot(index=\"hadm_id\", columns=\"label\", values=\"valuenum\")\n",
-    "        means = means.add_prefix(\"lab_\")\n",
-    "        means = pd.merge(\n",
-    "            patient_encounters[\n",
-    "                [\n",
-    "                    \"hadm_id\",\n",
-    "                    \"mortality_outcome\",\n",
-    "                    \"age\",\n",
-    "                    \"gender\",\n",
-    "                    \"admission_location\",\n",
-    "                ]\n",
-    "            ],\n",
-    "            means,\n",
-    "            on=\"hadm_id\",\n",
-    "        )\n",
-    "        means_df = pd.concat([means_df, means])\n",
-    "        if batch_num == 2:\n",
-    "            break\n",
-    "        print(\"Processing batch {}\".format(batch_num + 1))\n",
-    "\n",
-    "    return means_df\n",
-    "\n",
-    "\n",
-    "def run_query():\n",
-    "    \"\"\"Run query.\"\"\"\n",
-    "    cohort = get_encounters()\n",
-    "    cohort = compute_mortality_outcome(cohort)\n",
-    "\n",
-    "    return get_labevents(cohort)\n",
-    "\n",
-    "\n",
-    "cohort = run_query()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Inspection and Preprocessing\n",
-    "\n",
-    "### Drop NaNs based on the `NAN_THRESHOLD`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "null_counts = cohort.isnull().sum()[cohort.isnull().sum() > 0]\n",
-    "fig = go.Figure(data=[go.Bar(x=null_counts.index, y=null_counts.values)])\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    title=\"Number of Null Values per Column\",\n",
-    "    xaxis_title=\"Columns\",\n",
-    "    yaxis_title=\"Number of Null Values\",\n",
-    "    height=600,\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**\n",
-    "\n",
-    "We can use the log_plotly_figure method to add the figure to a section of the report. One can specify whether the figure should be interactive or not by setting the `interactive` parameter to `True` or `False` respectively. The default value is `True`. This\n",
-    "also affects the final size of the report. If the figure is interactive, the size of the report will be larger than if the figure is not interactive. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Number of Null Values per Column\",\n",
-    "    section_name=\"datasets\",\n",
-    "    interactive=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "thresh_nan = int(NAN_THRESHOLD * len(cohort))\n",
-    "cohort = cohort.dropna(axis=1, thresh=thresh_nan)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Outcome distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.pie(cohort, names=\"mortality_outcome\")\n",
-    "fig.update_traces(textinfo=\"percent+label\")\n",
-    "fig.update_layout(title_text=\"Outcome Distribution\")\n",
-    "fig.update_traces(\n",
-    "    hovertemplate=\"Outcome: %{label}<br>Count: \\\n",
-    "    %{value}<br>Percent: %{percent}\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Outcome Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The data is heavily unbalanced.\n",
-    "class_counts = cohort[\"mortality_outcome\"].value_counts()\n",
-    "class_ratio = class_counts[0] / class_counts[1]\n",
-    "print(class_ratio, class_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Gender distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.pie(cohort, names=\"gender\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Gender Distribution\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Gender Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###  Age distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(cohort, x=\"age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Age Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Identifying feature types\n",
-    "\n",
-    "Cyclops `TabularFeatures` class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features_list = set(cohort.columns.tolist()) - {\"hadm_id\", \"mortality_outcome\"}\n",
-    "features_list = sorted(features_list)\n",
-    "tab_features = TabularFeatures(\n",
-    "    data=cohort.reset_index(),\n",
-    "    features=features_list,\n",
-    "    by=\"hadm_id\",\n",
-    "    targets=\"mortality_outcome\",\n",
-    ")\n",
-    "print(tab_features.types)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating data preprocessors\n",
-    "\n",
-    "We create a data preprocessor using sklearn's ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numeric_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"mean\")), (\"scaler\", MinMaxScaler())],\n",
-    ")\n",
-    "binary_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"most_frequent\"))],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numeric_features = sorted((tab_features.features_by_type(\"numeric\")))\n",
-    "numeric_indices = [\n",
-    "    cohort[features_list].columns.get_loc(column) for column in numeric_features\n",
-    "]\n",
-    "print(numeric_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "binary_features = sorted(tab_features.features_by_type(\"binary\"))\n",
-    "ordinal_features = sorted(tab_features.features_by_type(\"ordinal\"))\n",
-    "binary_features.remove(\"mortality_outcome\")\n",
-    "binary_indices = [\n",
-    "    cohort[features_list].columns.get_loc(column) for column in binary_features\n",
-    "]\n",
-    "ordinal_indices = [\n",
-    "    cohort[features_list].columns.get_loc(column) for column in ordinal_features\n",
-    "]\n",
-    "print(binary_features, ordinal_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preprocessor = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        (\"num\", numeric_transformer, numeric_indices),\n",
-    "        (\n",
-    "            \"onehot\",\n",
-    "            OneHotEncoder(handle_unknown=\"ignore\"),\n",
-    "            binary_indices + ordinal_indices,\n",
-    "        ),\n",
-    "    ],\n",
-    "    remainder=\"passthrough\",\n",
-    ")\n",
-    "preprocessor_pipeline = [\n",
-    "    (\"preprocessor\", preprocessor),\n",
-    "    (\"oversampling\", SMOTE(random_state=RANDOM_SEED)),\n",
-    "]\n",
-    "preprocessor_pipeline = ImbPipeline(preprocessor_pipeline)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating Hugging Face Dataset\n",
-    "\n",
-    "We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cohort = cohort.drop(columns=[\"hadm_id\"])\n",
-    "dataset = Dataset.from_pandas(cohort)\n",
-    "dataset.cleanup_cache_files()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset = dataset.cast_column(\"mortality_outcome\", ClassLabel(num_classes=2))\n",
-    "dataset = dataset.train_test_split(\n",
-    "    train_size=TRAIN_SIZE,\n",
-    "    stratify_by_column=\"mortality_outcome\",\n",
-    "    seed=RANDOM_SEED,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model Creation\n",
-    "\n",
-    "CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a XGBoost classifier to fit a logisitic regression model. The model configurations can be passed to `create_model` based on the parameters for XGBClassifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_name = \"xgb_classifier\"\n",
-    "model = create_model(model_name, random_state=123)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Task Creation\n",
-    "\n",
-    "We use Cyclops tasks to define our model's task (in this case, BinaryTabularClassificationTask), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mortality_task = BinaryTabularClassificationTask(\n",
-    "    {model_name: model},\n",
-    "    task_features=features_list,\n",
-    "    task_target=\"mortality_outcome\",\n",
-    ")\n",
-    "mortality_task.list_models()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training\n",
-    "\n",
-    "If `best_model_params` is passed to the `train` method, the best model will be selected after the hyperparameter search. The parameters in `best_model_params` indicate the values to create the parameters grid.\n",
-    "\n",
-    "Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_model_params = {\n",
-    "    \"n_estimators\": [100, 250, 500],\n",
-    "    \"learning_rate\": [0.1, 0.01],\n",
-    "    \"max_depth\": [2, 5],\n",
-    "    \"reg_lambda\": [0, 1, 10],\n",
-    "    \"colsample_bytree\": [0.7, 0.8, 1],\n",
-    "    \"gamma\": [0, 1, 2, 10],\n",
-    "    \"method\": \"random\",\n",
-    "}\n",
-    "mortality_task.train(\n",
-    "    dataset[\"train\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor_pipeline,\n",
-    "    best_model_params=best_model_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_params = mortality_task.list_models_params()[model_name]\n",
-    "print(model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the model parameters to the report.**\n",
-    "\n",
-    "We can add model parameters to the model card using the `log_model_parameters` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_model_parameters(params=model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prediction\n",
-    "\n",
-    "The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = mortality_task.predict(\n",
-    "    dataset[\"test\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    proba=False,\n",
-    "    only_predictions=True,\n",
-    ")\n",
-    "print(len(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluation\n",
-    "\n",
-    "Evaluation is done using various evaluation metrics that provide different perspectives on the model's predictive abilities i.e. standard performance metrics and fairness metrics.\n",
-    "\n",
-    "The standard performance metrics can be created using the `MetricDict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_auroc\",\n",
-    "    \"binary_average_precision\",\n",
-    "    \"binary_roc_curve\",\n",
-    "    \"binary_precision_recall_curve\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using `SliceSpec` objects. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"min_value\": 20,\n",
-    "            \"max_value\": 50,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"min_value\": 50,\n",
-    "            \"max_value\": 80,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\"gender\": {\"value\": \"M\"}},\n",
-    "    {\"gender\": {\"value\": \"F\"}},\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `MetricDict` can also be defined for the fairness metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "specificity = create_metric(metric_name=\"binary_specificity\", experimental=True)\n",
-    "sensitivity = create_metric(metric_name=\"binary_sensitivity\", experimental=True)\n",
-    "fpr = -specificity + 1  # __rsub__ is not implemented for metrics\n",
-    "fnr = -sensitivity + 1\n",
-    "ber = (fpr + fnr) / 2\n",
-    "fairness_metric_collection = MetricDict(\n",
-    "    {\n",
-    "        \"Sensitivity\": sensitivity,\n",
-    "        \"Specificity\": specificity,\n",
-    "        \"BER\": ber,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The FairnessConfig helps in setting up and evaluating the fairness of the model predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fairness_config = FairnessConfig(\n",
-    "    metrics=fairness_metric_collection,\n",
-    "    dataset=None,  # dataset is passed from the evaluator\n",
-    "    target_columns=None,  # target columns are passed from the evaluator\n",
-    "    groups=[\"gender\", \"age\"],\n",
-    "    group_bins={\"age\": [20, 40]},\n",
-    "    group_base_values={\"age\": 40, \"gender\": \"M\"},\n",
-    "    thresholds=[0.5],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, dataset_with_preds = mortality_task.evaluate(\n",
-    "    dataset[\"test\"],\n",
-    "    metric_collection,\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=slice_spec,\n",
-    "    batch_size=-1,\n",
-    "    fairness_config=fairness_config,\n",
-    "    override_fairness_metrics=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the performance metrics to the report.**\n",
-    "\n",
-    "We can add a performance metric to the model card using the `log_performance_metric` method, which expects a dictionary where the keys are in the following format: `slice_name/metric_name`. For instance, `overall/accuracy`. \n",
-    "\n",
-    "We first need to process the evaluation results to get the metrics in the right format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_name = f\"model_for_preds.{model_name}\"\n",
-    "results_flat = flatten_results_dict(\n",
-    "    results=results,\n",
-    "    remove_metrics=[\"BinaryROC\", \"BinaryPrecisionRecallCurve\"],\n",
-    "    model_name=model_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ruff: noqa: W505\n",
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the `ClassificationPlotter` to plot the performance metrics and the add the figure to the model card using the `log_plotly_figure` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the ROC curves and AUROC results for all the slices\n",
-    "roc_curves = {\n",
-    "    slice_name: slice_results[\"BinaryROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "aurocs = {\n",
-    "    slice_name: slice_results[\"BinaryAUROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "roc_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the precision-recall curves and average precision results for all the slices\n",
-    "pr_curves = {\n",
-    "    slice_name: slice_results[\"BinaryPrecisionRecallCurve\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "average_precisions = {\n",
-    "    slice_name: slice_results[\"BinaryAveragePrecision\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "pr_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the ROC curves for all the slices\n",
-    "roc_plot = plotter.roc_curve_comparison(roc_curves, aurocs=aurocs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=roc_plot,\n",
-    "    caption=\"ROC Curve Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "roc_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the precision-recall curves for all the slices\n",
-    "pr_plot = plotter.precision_recall_curve_comparison(\n",
-    "    pr_curves,\n",
-    "    auprcs=average_precisions,\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=pr_plot,\n",
-    "    caption=\"Precision-Recall Curve Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "pr_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the overall classification metric values.\n",
-    "overall_performance = {\n",
-    "    metric_name: metric_value\n",
-    "    for metric_name, metric_value in results[model_name][\"overall\"].items()\n",
-    "    if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the overall classification metric values.\n",
-    "overall_performance_plot = plotter.metrics_value(\n",
-    "    overall_performance,\n",
-    "    title=\"Overall Performance\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=overall_performance_plot,\n",
-    "    caption=\"Overall Performance\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "overall_performance_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=slice_metrics_plot,\n",
-    "    caption=\"Slice Metric Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reformatting the fairness metrics\n",
-    "fairness_results = copy.deepcopy(results[\"fairness\"])\n",
-    "fairness_metrics = {}\n",
-    "# remove the group size from the fairness results and add it to the slice name\n",
-    "for slice_name, slice_results in fairness_results.items():\n",
-    "    group_size = slice_results.pop(\"Group Size\")\n",
-    "    fairness_metrics[f\"{slice_name} (Size={group_size})\"] = slice_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the fairness metrics\n",
-    "fairness_plot = plotter.metrics_comparison_scatter(\n",
-    "    fairness_metrics,\n",
-    "    title=\"Fairness Metrics\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fairness_plot,\n",
-    "    caption=\"Fairness Metrics\",\n",
-    "    section_name=\"fairness analysis\",\n",
-    ")\n",
-    "fairness_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Report Generation\n",
-    "\n",
-    "Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.\n",
-    "\n",
-    "\n",
-    "Let's start with populating the model details section, which includes the following fields by default:\n",
-    "- description: A high-level description of the model and its usage for a general audience.\n",
-    "- version: The version of the model.\n",
-    "- owners: The individuals or organizations that own the model.\n",
-    "- license: The license under which the model is made available.\n",
-    "- citation: The citation for the model.\n",
-    "- references: Links to resources that are relevant to the model.\n",
-    "- path: The path to where the model is stored.\n",
-    "- regulatory_requirements: The regulatory requirements that are relevant to the model.\n",
-    "\n",
-    "We can add additional fields to the model details section by passing a dictionary to the `log_from_dict` method and specifying the section name as `model_details`. You can also use the `log_descriptor` method to add a new field object with a `description` attribute to any section of the model card."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"Mortality Prediction Model\",\n",
-    "        \"description\": \"The model was trained on the MIMICIV dataset \\\n",
-    "            to predict risk of in-hospital mortality.\",\n",
-    "    },\n",
-    "    section_name=\"model_details\",\n",
-    ")\n",
-    "report.log_version(\n",
-    "    version_str=\"0.0.1\",\n",
-    "    date=str(date.today()),\n",
-    "    description=\"Initial Release\",\n",
-    ")\n",
-    "report.log_owner(\n",
-    "    name=\"CyclOps Team\",\n",
-    "    contact=\"vectorinstitute.github.io/cyclops/\",\n",
-    "    email=\"cyclops@vectorinstitute.ai\",\n",
-    ")\n",
-    "report.log_license(identifier=\"Apache-2.0\")\n",
-    "report.log_reference(\n",
-    "    link=\"https://xgboost.readthedocs.io/en/stable/python/python_api.html\",  # noqa: E501\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, let's populate the considerations section, which includes the following fields by default:\n",
-    "- users: The intended users of the model.\n",
-    "- use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases.\n",
-    "- fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms.\n",
-    "- ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This can be populated using the  `log_risk` method.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"users\": [\n",
-    "            {\"description\": \"Hospitals\"},\n",
-    "            {\"description\": \"Clinicians\"},\n",
-    "        ],\n",
-    "    },\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_user(description=\"ML Engineers\")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting prolonged length of stay\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"sex, age\",\n",
-    "    benefit=\"Improved health outcomes for patients.\",\n",
-    "    harm=\"Biased predictions for patients in certain groups (e.g. older patients) \\\n",
-    "        may lead to worse health outcomes.\",\n",
-    "    mitigation_strategy=\"We will monitor the performance of the model on these groups \\\n",
-    "        and retrain the model if the performance drops below a certain threshold.\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"The model may be used to make decisions that affect the health of patients.\",\n",
-    "    mitigation_strategy=\"The model should be continuously monitored for performance \\\n",
-    "        and retrained if the performance drops below a certain threshold.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the model card is populated, you can generate the report using the `export` method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named `cyclops_reports` in the current working directory. You can change the path by passing a `output_dir` argument when instantiating the `ModelCardReport` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "synthetic_timestamps = [\n",
-    "    \"2021-09-01\",\n",
-    "    \"2021-10-01\",\n",
-    "    \"2021-11-01\",\n",
-    "    \"2021-12-01\",\n",
-    "    \"2022-01-01\",\n",
-    "]\n",
-    "report._model_card.overview = None\n",
-    "report_path = report.export(\n",
-    "    output_filename=\"mortality_report_periodic.html\",\n",
-    "    synthetic_timestamp=synthetic_timestamps[0],\n",
-    ")\n",
-    "shutil.copy(f\"{report_path}\", \".\")\n",
-    "for i in range(4):\n",
-    "    report._model_card.overview = None\n",
-    "    for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "        metric.value = np.clip(\n",
-    "            metric.value + np.random.normal(0, 0.1),\n",
-    "            0,\n",
-    "            1,\n",
-    "        )\n",
-    "        metric.tests[0].passed = bool(metric.value >= 0.7)\n",
-    "    report_path = report.export(\n",
-    "        output_filename=\"mortality_report_periodic.html\",\n",
-    "        synthetic_timestamp=synthetic_timestamps[i + 1],\n",
-    "    )\n",
-    "    shutil.copy(f\"{report_path}\", \".\")\n",
-    "shutil.rmtree(\"./cyclops_report\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can view the generated HTML [report](./mortality_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt b/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt
deleted file mode 100644
index e2cbed78e..000000000
--- a/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt
+++ /dev/null
@@ -1,611 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ea16a542",
-   "metadata": {},
-   "source": [
-    "# Chest X-Ray Disease Classification\n",
-    "\n",
-    "This notebook shows chest x-ray classification on the [NIH dataset](https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community) using a pretrained model from the TorchXRayVision library and CyclOps to generate a model card."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb698a85",
-   "metadata": {},
-   "source": [
-    "### Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fc1eb72a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"Chest X-ray Disease Classification.\"\"\"\n",
-    "\n",
-    "import shutil\n",
-    "from functools import partial\n",
-    "\n",
-    "import numpy as np\n",
-    "import plotly.express as px\n",
-    "from torchvision.transforms import Compose\n",
-    "from torchxrayvision.models import DenseNet\n",
-    "\n",
-    "from cyclops.data.loader import load_nihcxr\n",
-    "from cyclops.data.slicer import (\n",
-    "    SliceSpec,\n",
-    "    filter_value,  # noqa: E402\n",
-    ")\n",
-    "from cyclops.data.transforms import Lambdad, Resized\n",
-    "from cyclops.data.utils import apply_transforms\n",
-    "from cyclops.evaluate import evaluator\n",
-    "from cyclops.evaluate.metrics.factory import create_metric\n",
-    "from cyclops.models.wrappers import PTModel\n",
-    "from cyclops.report import ModelCardReport"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac67f0e7",
-   "metadata": {},
-   "source": [
-    "## Generate Historical Reports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25c2a16f",
-   "metadata": {
-    "nbsphinx": "hidden"
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"Generate historical reports with validation data\n",
-    "for comparison with periodic report on test data.\"\"\"\n",
-    "\n",
-    "!python3 generate_nihcxr_report.py --synthetic_timestamp \"2023-10-19\" --seed 43\n",
-    "!python3 generate_nihcxr_report.py --synthetic_timestamp \"2023-10-16\" --seed 44\n",
-    "!python3 generate_nihcxr_report.py --synthetic_timestamp \"2023-10-22\" --seed 45\n",
-    "!python3 generate_nihcxr_report.py --synthetic_timestamp \"2023-10-30\" --seed 46"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "198896c7",
-   "metadata": {},
-   "source": [
-    "CyclOps offers a package for documentation of the model through a model report. The `ModelCardReport` class is used to populate and generate the model report as an HTML file. The model report has the following sections:\n",
-    "\n",
-    "- Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).\n",
-    "- Datasets: High level statistics of the training data, including changes in distribution over time.\n",
-    "- Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.\n",
-    "- Fairness Analysis: This section contains the fairness metrics of the model.\n",
-    "- Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.\n",
-    "- Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.\n",
-    "- Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.\n",
-    "\n",
-    "We will use this to document the model development process as we go along and generate the model report at the end.\n",
-    "\n",
-    "`The model report tool is a work in progress and is subject to change.`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ee175a9",
-   "metadata": {},
-   "source": [
-    "## Initialize Periodic Report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "03edf1c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f8e8915",
-   "metadata": {},
-   "source": [
-    "### Load Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6514120e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dir = \"/mnt/data/clinical_datasets/NIHCXR\"\n",
-    "nih_ds = load_nihcxr(data_dir)[\"test\"]\n",
-    "nih_ds = nih_ds.select(range(1000))\n",
-    "\n",
-    "transforms = Compose(\n",
-    "    [\n",
-    "        Resized(\n",
-    "            keys=(\"image\",),\n",
-    "            spatial_size=(224, 224),\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "        Lambdad(\n",
-    "            keys=(\"image\",),\n",
-    "            func=lambda x: ((2 * (x / 255.0)) - 1.0) * 1024,\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "        Lambdad(\n",
-    "            keys=(\"image\",),\n",
-    "            func=lambda x: x[0][np.newaxis, :] if x.shape[0] != 1 else x,\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "    ],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7c9172a",
-   "metadata": {},
-   "source": [
-    "## Model Creation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5f624ed4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = PTModel(DenseNet(weights=\"densenet121-res224-nih\"))\n",
-    "\n",
-    "model.initialize()\n",
-    "nih_ds = model.predict(\n",
-    "    nih_ds,\n",
-    "    feature_columns=[\"image\"],\n",
-    "    transforms=partial(apply_transforms, transforms=transforms),\n",
-    "    model_name=\"densenet\",\n",
-    ")\n",
-    "\n",
-    "# remove any rows with No Finding == 1\n",
-    "nih_ds = nih_ds.filter(\n",
-    "    partial(filter_value, column_name=\"No Finding\", value=1, negate=True),\n",
-    "    batched=True,\n",
-    ")\n",
-    "\n",
-    "# remove the No Finding column and adjust the predictions to account for it\n",
-    "nih_ds = nih_ds.map(\n",
-    "    lambda x: {\n",
-    "        \"predictions.densenet\": x[\"predictions.densenet\"][:14],\n",
-    "    },\n",
-    "    remove_columns=[\"No Finding\"],\n",
-    ")\n",
-    "print(nih_ds.features)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9743a047",
-   "metadata": {},
-   "source": [
-    "### Multilabel AUROC by Pathology and Sex"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bff27cc1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pathologies = model.model.pathologies[:14]\n",
-    "\n",
-    "# define the slices\n",
-    "slices = [\n",
-    "    {\"Patient Gender\": {\"value\": \"M\"}},\n",
-    "    {\"Patient Gender\": {\"value\": \"F\"}},\n",
-    "]\n",
-    "\n",
-    "\n",
-    "num_labels = len(pathologies)\n",
-    "ppv = create_metric(\n",
-    "    metric_name=\"multilabel_ppv\",\n",
-    "    experimental=True,\n",
-    "    num_labels=num_labels,\n",
-    "    average=None,\n",
-    ")\n",
-    "\n",
-    "npv = create_metric(\n",
-    "    metric_name=\"multilabel_npv\",\n",
-    "    experimental=True,\n",
-    "    num_labels=num_labels,\n",
-    "    average=None,\n",
-    ")\n",
-    "\n",
-    "specificity = create_metric(\n",
-    "    metric_name=\"multilabel_specificity\",\n",
-    "    experimental=True,\n",
-    "    num_labels=num_labels,\n",
-    "    average=None,\n",
-    ")\n",
-    "\n",
-    "sensitivity = create_metric(\n",
-    "    metric_name=\"multilabel_sensitivity\",\n",
-    "    experimental=True,\n",
-    "    num_labels=num_labels,\n",
-    "    average=None,\n",
-    ")\n",
-    "\n",
-    "# create the slice functions\n",
-    "slice_spec = SliceSpec(spec_list=slices)\n",
-    "\n",
-    "nih_eval_results_gender = evaluator.evaluate(\n",
-    "    dataset=nih_ds,\n",
-    "    metrics=[ppv, npv, sensitivity, specificity],\n",
-    "    target_columns=pathologies,\n",
-    "    prediction_columns=\"predictions.densenet\",\n",
-    "    ignore_columns=\"image\",\n",
-    "    slice_spec=slice_spec,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "88448ced",
-   "metadata": {},
-   "source": [
-    "### Multilabel AUROC by Pathology and Age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c38ef9e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define the slices\n",
-    "slices = [\n",
-    "    {\"Patient Age\": {\"min_value\": 19, \"max_value\": 35}},\n",
-    "    {\"Patient Age\": {\"min_value\": 35, \"max_value\": 65}},\n",
-    "    {\"Patient Age\": {\"min_value\": 65, \"max_value\": 100}},\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 19, \"max_value\": 35},\n",
-    "        \"Patient Gender\": {\"value\": \"M\"},\n",
-    "    },\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 19, \"max_value\": 35},\n",
-    "        \"Patient Gender\": {\"value\": \"F\"},\n",
-    "    },\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 35, \"max_value\": 65},\n",
-    "        \"Patient Gender\": {\"value\": \"M\"},\n",
-    "    },\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 35, \"max_value\": 65},\n",
-    "        \"Patient Gender\": {\"value\": \"F\"},\n",
-    "    },\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 65, \"max_value\": 100},\n",
-    "        \"Patient Gender\": {\"value\": \"M\"},\n",
-    "    },\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 65, \"max_value\": 100},\n",
-    "        \"Patient Gender\": {\"value\": \"F\"},\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# create the slice functions\n",
-    "slice_spec = SliceSpec(spec_list=slices)\n",
-    "\n",
-    "nih_eval_results_age = evaluator.evaluate(\n",
-    "    dataset=nih_ds,\n",
-    "    metrics=[ppv, npv, sensitivity, specificity],\n",
-    "    target_columns=pathologies,\n",
-    "    prediction_columns=\"predictions.densenet\",\n",
-    "    ignore_columns=\"image\",\n",
-    "    slice_spec=slice_spec,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3e674b7a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.pie(\n",
-    "    values=[nih_ds[\"Patient Gender\"].count(\"M\"), nih_ds[\"Patient Gender\"].count(\"F\")],\n",
-    "    names=[\"Male\", \"Female\"],\n",
-    ")\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    title=\"Gender Distribution\",\n",
-    ")\n",
-    "\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Gender Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a144dc9a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(nih_ds[\"Patient Age\"])\n",
-    "fig.update_traces(showlegend=False)\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Age Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d2ff6506",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.bar(x=pathologies, y=[np.array(nih_ds[p]).sum() for p in pathologies])\n",
-    "fig.update_layout(\n",
-    "    title=\"Pathology Distribution\",\n",
-    "    xaxis_title=\"Pathology\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    "    # change size of plot\n",
-    ")\n",
-    "\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Pathology Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5eec4e5",
-   "metadata": {},
-   "source": [
-    "### Log Performance Metrics as Tests w/ Thresholds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec627997",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = {}\n",
-    "for slice_, metrics in nih_eval_results_age[\"model_for_predictions.densenet\"].items():\n",
-    "    for name, metric in metrics.items():\n",
-    "        results_flat[f\"{slice_}/{name}\"] = metric.mean()\n",
-    "        for itr, m in enumerate(metric):\n",
-    "            if slice_ == \"overall\":\n",
-    "                results_flat[f\"pathology:{pathologies[itr]}/{name}\"] = m\n",
-    "            else:\n",
-    "                results_flat[f\"{slice_}&pathology:{pathologies[itr]}/{name}\"] = m\n",
-    "for slice_, metrics in nih_eval_results_gender[\n",
-    "    \"model_for_predictions.densenet\"\n",
-    "].items():\n",
-    "    for name, metric in metrics.items():\n",
-    "        results_flat[f\"{slice_}/{name}\"] = metric.mean()\n",
-    "        for itr, m in enumerate(metric):\n",
-    "            if slice_ == \"overall\":\n",
-    "                results_flat[f\"pathology:{pathologies[itr]}/{name}\"] = m\n",
-    "            else:\n",
-    "                results_flat[f\"{slice_}&pathology:{pathologies[itr]}/{name}\"] = m\n",
-    "\n",
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"MultilabelPPV\": \"The proportion of correctly predicted positive instances among all instances predicted as positive. Also known as precision.\",\n",
-    "        \"MultilabelNPV\": \"The proportion of correctly predicted negative instances among all instances predicted as negative.\",\n",
-    "        \"MultilabelSensitivity\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"MultilabelSpecificity\": \"The proportion of actual negative instances that are correctly predicted.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist() if isinstance(metric, np.generic) else metric,\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff0664e9",
-   "metadata": {},
-   "source": [
-    "## Populate Model Card Fields"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c9d6fe83",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# model details for NIH Chest X-Ray model\n",
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"NIH Chest X-Ray Multi-label Classification Model\",\n",
-    "        \"description\": \"This model is a DenseNet121 model trained on the NIH Chest \\\n",
-    "            X-Ray dataset, which contains 112,120 frontal-view X-ray images of 30,805 \\\n",
-    "            unique patients with the fourteen text-mined disease labels from the \\\n",
-    "            associated radiological reports. The labels are Atelectasis, Cardiomegaly, \\\n",
-    "            Effusion, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, \\\n",
-    "            Consolidation, Edema, Emphysema, Fibrosis, Pleural Thickening, and Hernia. \\\n",
-    "            The model was trained on 80% of the data and evaluated on the remaining \\\n",
-    "            20%.\",\n",
-    "        \"references\": [{\"link\": \"https://arxiv.org/abs/2111.00595\"}],\n",
-    "    },\n",
-    "    section_name=\"Model Details\",\n",
-    ")\n",
-    "\n",
-    "report.log_citation(\n",
-    "    citation=\"\"\"@inproceedings{Cohen2022xrv,\n",
-    "    title = {{TorchXRayVision: A library of chest X-ray datasets and models}},\n",
-    "    author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, \\\n",
-    "    Paul and Morrison,Paul and Torabian, Parsa and Guarrera, \\\n",
-    "    Matteo and Lungren, Matthew P and Chaudhari,\\\n",
-    "    Akshay and Brooks, Rupert and Hashir, \\\n",
-    "    Mohammad and Bertrand, Hadrien},\n",
-    "    booktitle = {Medical Imaging with Deep Learning},\n",
-    "    url = {https://github.com/mlmed/torchxrayvision},\n",
-    "    arxivId = {2111.00595},\n",
-    "    year = {2022}\n",
-    "    }\"\"\",\n",
-    ")\n",
-    "\n",
-    "report.log_citation(\n",
-    "    citation=\"\"\"@inproceedings{cohen2020limits,\n",
-    "    title={On the limits of cross-domain generalization\\\n",
-    "            in automated X-ray prediction},\n",
-    "    author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, \\\n",
-    "        Rupert and Bertrand, Hadrien},\n",
-    "    booktitle={Medical Imaging with Deep Learning},\n",
-    "    year={2020},\n",
-    "    url={https://arxiv.org/abs/2002.02497}\n",
-    "    }\"\"\",\n",
-    ")\n",
-    "\n",
-    "report.log_owner(\n",
-    "    name=\"Machine Learning and Medicine Lab\",\n",
-    "    contact=\"mlmed.org\",\n",
-    "    email=\"joseph@josephpcohen.com\",\n",
-    ")\n",
-    "\n",
-    "# considerations\n",
-    "report.log_user(description=\"Radiologists\")\n",
-    "report.log_user(description=\"Data Scientists\")\n",
-    "\n",
-    "report.log_use_case(\n",
-    "    description=\"The model can be used to predict the presence of 14 pathologies \\\n",
-    "        in chest X-ray images.\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_descriptor(\n",
-    "    name=\"limitations\",\n",
-    "    description=\"The limitations of this model include its inability to detect \\\n",
-    "                pathologies that are not included in the 14 labels of the NIH \\\n",
-    "                Chest X-Ray dataset. Additionally, the model may not perform \\\n",
-    "                well on images that are of poor quality or that contain \\\n",
-    "                artifacts. Finally, the model may not generalize well to\\\n",
-    "                populations that are not well-represented in the training \\\n",
-    "                data, such as patients from different geographic regions or \\\n",
-    "                with different demographics.\",\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_descriptor(\n",
-    "    name=\"tradeoffs\",\n",
-    "    description=\"The model can help radiologists to detect pathologies in \\\n",
-    "        chest X-ray images, but it may not generalize well to populations \\\n",
-    "        that are not well-represented in the training data.\",\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"One ethical risk of the model is that it may not generalize well to \\\n",
-    "        populations that are not well-represented in the training data,\\\n",
-    "        such as patients from different geographic regions \\\n",
-    "        or with different demographics. \",\n",
-    "    mitigation_strategy=\"A mitigation strategy for this risk is to ensure \\\n",
-    "        that the training data is diverse and representative of the population \\\n",
-    "            that the model will be used on. Additionally, the model should be \\\n",
-    "            regularly evaluated and updated to ensure that it continues to \\\n",
-    "            perform well on diverse populations. Finally, the model should \\\n",
-    "            be used in conjunction with human expertise to ensure that \\\n",
-    "            any biases or limitations are identified and addressed.\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"Patients with rare pathologies\",\n",
-    "    benefit=\"The model can help radiologists to detect pathologies in \\\n",
-    "        chest X-ray images.\",\n",
-    "    harm=\"The model may not generalize well to populations that are not \\\n",
-    "        well-represented in the training data.\",\n",
-    "    mitigation_strategy=\"A mitigation strategy for this risk is to ensure that \\\n",
-    "        the training data is diverse and representative of the population.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ae088e5b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report_path = report.export(\n",
-    "    output_filename=\"nihcxr_report_periodic.html\",\n",
-    "    synthetic_timestamp=\"2023-11-06\",\n",
-    ")\n",
-    "shutil.copy(f\"{report_path}\", \".\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7545d39",
-   "metadata": {},
-   "source": [
-    "You can view the generated HTML [report](./nihcxr_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/api/_sources/tutorials/nihcxr/monitor_api.ipynb.txt b/api/_sources/tutorials/nihcxr/monitor_api.ipynb.txt
deleted file mode 100644
index 53e9ebcd3..000000000
--- a/api/_sources/tutorials/nihcxr/monitor_api.ipynb.txt
+++ /dev/null
@@ -1,301 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6b2520a8-d4ad-4941-8ea7-71fdd631225f",
-   "metadata": {},
-   "source": [
-    "# NIHCXR Clinical Drift Experiments Tutorial"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "275fdb05",
-   "metadata": {},
-   "source": [
-    "## Import Libraries and Load NIHCXR Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8aa3302d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"NIHCXR Clinical Drift Experiments Tutorial.\"\"\"\n",
-    "\n",
-    "\n",
-    "import random\n",
-    "\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "from torchvision.transforms import Compose\n",
-    "from torchxrayvision.models import DenseNet\n",
-    "\n",
-    "from cyclops.data.loader import load_nihcxr\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.data.transforms import Lambdad, Resized\n",
-    "from cyclops.monitor import ClinicalShiftApplicator, Detector, Reductor, TSTester\n",
-    "from cyclops.monitor.plotter import plot_drift_experiment, plot_drift_timeseries\n",
-    "\n",
-    "\n",
-    "nih_ds = load_nihcxr(\"/mnt/data/clinical_datasets/NIHCXR\")[\"test\"]\n",
-    "\n",
-    "random.seed(42)\n",
-    "np.random.seed(42)\n",
-    "torch.manual_seed(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebcc72bb",
-   "metadata": {},
-   "source": [
-    "## Example 1. Generate Source/Target Dataset for Experiments (1-2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e11920db",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "shifter = ClinicalShiftApplicator(\n",
-    "    \"sex\",\n",
-    "    source=None,\n",
-    "    target=\"F\",\n",
-    "    shift_id=\"Patient Gender\",\n",
-    ")\n",
-    "source_ds, target_ds = shifter.apply_shift(nih_ds, num_proc=6)\n",
-    "\n",
-    "transforms = Compose(\n",
-    "    [\n",
-    "        Resized(\n",
-    "            spatial_size=(224, 224),\n",
-    "            keys=(\"image\",),\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "        Lambdad(\n",
-    "            func=lambda x: ((2 * (x / 255.0)) - 1.0) * 1024,\n",
-    "            keys=(\"image\",),\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "        Lambdad(\n",
-    "            func=lambda x: x[0][np.newaxis, :] if x.shape[0] != 1 else x,\n",
-    "            keys=(\"image\",),\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "    ],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ab403dc5",
-   "metadata": {},
-   "source": [
-    "## Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "54a3523a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = DenseNet(weights=\"densenet121-res224-all\")\n",
-    "dr_methods = {\n",
-    "    \"BBSE\": \"bbse-soft\",\n",
-    "    \"BBSE + TXRV-AE\": \"bbse-soft+txrv-ae\",\n",
-    "    \"TXRV-AE\": \"txrv-ae\",\n",
-    "}\n",
-    "results = {}\n",
-    "\n",
-    "for name, dr_method in dr_methods.items():\n",
-    "    if name == \"TXRV-AE\":\n",
-    "        reductor = Reductor(\n",
-    "            dr_method=dr_method,\n",
-    "            transforms=transforms,\n",
-    "            feature_columns=[\"image\"],\n",
-    "        )\n",
-    "    else:\n",
-    "        reductor = Reductor(\n",
-    "            dr_method=dr_method,\n",
-    "            model=model,\n",
-    "            transforms=transforms,\n",
-    "            feature_columns=[\"image\"],\n",
-    "        )\n",
-    "    detector = Detector(\n",
-    "        \"sensitivity_test\",\n",
-    "        reductor=reductor,\n",
-    "        tester=TSTester(tester_method=\"ks\"),\n",
-    "        source_sample_size=50,\n",
-    "        target_sample_size=[10, 25, 50],\n",
-    "        num_runs=1,\n",
-    "    )\n",
-    "    result = detector.detect_shift(source_ds, target_ds)\n",
-    "    results[name] = result\n",
-    "plot_drift_experiment(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec20a728",
-   "metadata": {},
-   "source": [
-    "## Example 3. Sensitivity test experiment with models trained on different datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "40b5a90f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models = {\n",
-    "    \"MODEL: NIH\": \"densenet121-res224-nih\",\n",
-    "    \"MODEL: CHEXPERT\": \"densenet121-res224-chex\",\n",
-    "    \"MODEL: PADCHEST\": \"densenet121-res224-pc\",\n",
-    "}\n",
-    "results = {}\n",
-    "\n",
-    "for model_name, model in models.items():\n",
-    "    detector = Detector(\n",
-    "        \"sensitivity_test\",\n",
-    "        reductor=Reductor(\n",
-    "            dr_method=\"bbse-soft\",\n",
-    "            model=DenseNet(weights=model),\n",
-    "            transforms=transforms,\n",
-    "            feature_columns=[\"image\"],\n",
-    "        ),\n",
-    "        tester=TSTester(tester_method=\"ks\"),\n",
-    "        source_sample_size=50,\n",
-    "        target_sample_size=[10, 25, 50],\n",
-    "        num_runs=1,\n",
-    "    )\n",
-    "    results[model_name] = detector.detect_shift(source_ds, target_ds)\n",
-    "plot_drift_experiment(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e8ebcf1",
-   "metadata": {},
-   "source": [
-    "## Example 4. Sensitivity test experiment with different clinical shifts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9ba03fac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = DenseNet(weights=\"densenet121-res224-all\")\n",
-    "source_slice = None\n",
-    "target_slices = {\n",
-    "    \"SEX: MALE\": SliceSpec(spec_list=[{\"Patient Gender\": {\"value\": \"M\"}}]),\n",
-    "    \"SEX: FEMALE\": SliceSpec(spec_list=[{\"Patient Gender\": {\"value\": \"F\"}}]),\n",
-    "    \"AGE: 18-35\": SliceSpec(\n",
-    "        spec_list=[{\"Patient Age\": {\"min_value\": 18, \"max_value\": 35}}],\n",
-    "    ),\n",
-    "    \"AGE: 35-65\": SliceSpec(\n",
-    "        spec_list=[{\"Patient Age\": {\"min_value\": 35, \"max_value\": 65}}],\n",
-    "    ),\n",
-    "}\n",
-    "results = {}\n",
-    "\n",
-    "for name, target_slice in target_slices.items():\n",
-    "    source_slice = None\n",
-    "    shifter = ClinicalShiftApplicator(\n",
-    "        \"custom\",\n",
-    "        source=source_slice,\n",
-    "        target=target_slice,\n",
-    "    )\n",
-    "    ds_source, ds_target = shifter.apply_shift(nih_ds, num_proc=6)\n",
-    "\n",
-    "    detector = Detector(\n",
-    "        \"sensitivity_test\",\n",
-    "        reductor=Reductor(\n",
-    "            dr_method=\"bbse-soft\",\n",
-    "            model=model,\n",
-    "            transforms=transforms,\n",
-    "            feature_columns=[\"image\"],\n",
-    "        ),\n",
-    "        tester=TSTester(tester_method=\"ks\"),\n",
-    "        source_sample_size=50,\n",
-    "        target_sample_size=[10, 25, 50],\n",
-    "        num_runs=1,\n",
-    "    )\n",
-    "    results[name] = detector.detect_shift(ds_source, ds_target)\n",
-    "plot_drift_experiment(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7571bc9f",
-   "metadata": {},
-   "source": [
-    "## Example 5. Rolling window experiment with synthetic timestamps using biweekly window"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "77e4b383",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model = DenseNet(weights=\"densenet121-res224-all\")\n",
-    "detector = Detector(\n",
-    "    \"rolling_window_drift\",\n",
-    "    reductor=Reductor(\n",
-    "        dr_method=\"bbse-soft\",\n",
-    "        model=model,\n",
-    "        transforms=transforms,\n",
-    "        feature_columns=[\"image\"],\n",
-    "    ),\n",
-    "    tester=TSTester(tester_method=\"ks\"),\n",
-    "    source_sample_size=50,\n",
-    "    target_sample_size=10,\n",
-    "    timestamp_column=\"timestamp\",\n",
-    "    window_size=\"4W\",\n",
-    ")\n",
-    "\n",
-    "results = detector.detect_shift(source_ds, target_ds)\n",
-    "plot_drift_timeseries(results)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "nbsphinx": {
-   "execute": "never"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/api/_sources/tutorials/synthea/los_prediction.ipynb.txt b/api/_sources/tutorials/synthea/los_prediction.ipynb.txt
deleted file mode 100644
index 53d4e4304..000000000
--- a/api/_sources/tutorials/synthea/los_prediction.ipynb.txt
+++ /dev/null
@@ -1,1506 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e337389b-1cfe-4796-a846-b4e1ba5690d6",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Prolonged Length of Stay Prediction\n",
-    "\n",
-    "This notebook showcases length of stay prediction on the [Synthea](https://github.com/synthetichealth/synthea) dataset using CyclOps. The task is formulated as a binary classification task, where we predict the probability that a patient will stay 7 days or longer.\n",
-    "\n",
-    "To generate the synthetic patient data:\n",
-    "\n",
-    "1. Generate synthea data using their releases. We used [v3.0.0](https://github.com/synthetichealth/synthea/releases/tag/v3.0.0).\n",
-    "2. Follow instructions provided in [ETL-Synthea](https://github.com/OHDSI/ETL-Synthea) to load the CSV data into a postgres database."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12c18656-7f16-4230-85d0-944563d6a13e",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53009e6b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"Prolonged Length of Stay Prediction.\"\"\"\n",
-    "\n",
-    "import copy\n",
-    "import shutil\n",
-    "from datetime import date\n",
-    "\n",
-    "import cycquery.ops as qo\n",
-    "import numpy as np\n",
-    "import plotly.express as px\n",
-    "import plotly.graph_objects as go\n",
-    "from cycquery import DatasetQuerier\n",
-    "from datasets import Dataset\n",
-    "from datasets.features import ClassLabel\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
-    "\n",
-    "from cyclops.data.df.feature import TabularFeatures\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate.fairness import FairnessConfig  # noqa: E402\n",
-    "from cyclops.evaluate.metrics import create_metric\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.models.catalog import create_model\n",
-    "from cyclops.report import ModelCardReport\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter\n",
-    "from cyclops.report.utils import flatten_results_dict\n",
-    "from cyclops.tasks import BinaryTabularClassificationTask"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0c35352-ccef-47c9-8d1a-3062c62adb97",
-   "metadata": {},
-   "source": [
-    "CyclOps offers a package for documentation of the model through a model report. The `ModelCardReport` class is used to populate and generate the model report as an HTML file. The model report has the following sections:\n",
-    "\n",
-    "- Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).\n",
-    "- Datasets: High level statistics of the training data, including changes in distribution over time.\n",
-    "- Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.\n",
-    "- Fairness Analysis: This section contains the fairness metrics of the model.\n",
-    "- Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.\n",
-    "- Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.\n",
-    "- Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.\n",
-    "\n",
-    "We will use this to document the model development process as we go along and generate the model report at the end.\n",
-    "\n",
-    "`The model report tool is a work in progress and is subject to change.`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "afae58a8-5708-4e05-8695-25ba3ce1a71f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28becf40-dc5f-4a1d-a1c9-5d6a41f797aa",
-   "metadata": {},
-   "source": [
-    "## Constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "739b109a-011b-4e6e-a3de-964edeffddbd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "NAN_THRESHOLD = 0.25\n",
-    "NUM_DAYS = 7\n",
-    "TRAIN_SIZE = 0.8\n",
-    "RANDOM_SEED = 85"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52d5aa23-6ae5-4ab6-824f-b68c47f471ae",
-   "metadata": {},
-   "source": [
-    "## Data Querying\n",
-    "\n",
-    "### Compute length of stay (labels)\n",
-    "\n",
-    "1. Get encounters, compute length of stay.\n",
-    "2. Filter out encounters less than 2 days and greater than 20 days."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e497df9f-0f3d-4e9c-845c-539627a37f67",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "querier = DatasetQuerier(\n",
-    "    dbms=\"postgresql\",\n",
-    "    port=5432,\n",
-    "    host=\"localhost\",\n",
-    "    database=\"synthea_demo\",\n",
-    "    user=\"postgres\",\n",
-    "    password=\"pwd\",\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def get_encounters():\n",
-    "    \"\"\"Get encounters data.\"\"\"\n",
-    "    patients = querier.native.patients()\n",
-    "    ops = qo.Sequential(\n",
-    "        qo.Rename({\"id\": \"patient_id\"}),\n",
-    "        qo.Keep([\"patient_id\", \"birthdate\", \"gender\", \"race\", \"ethnicity\"]),\n",
-    "    )\n",
-    "    patients = patients.ops(ops)\n",
-    "    encounters = querier.native.encounters()\n",
-    "    patient_encounters = encounters.join(\n",
-    "        patients,\n",
-    "        on=(\"patient\", \"patient_id\"),\n",
-    "        isouter=True,\n",
-    "    )\n",
-    "    ops = qo.Sequential(\n",
-    "        qo.Rename({\"id\": \"encounter_id\"}),\n",
-    "        qo.ExtractTimestampComponent(\"start\", \"year\", \"start_year\"),\n",
-    "        qo.ExtractTimestampComponent(\"birthdate\", \"year\", \"birthdate_year\"),\n",
-    "        qo.AddColumn(\n",
-    "            \"start_year\",\n",
-    "            \"birthdate_year\",\n",
-    "            new_col_labels=\"age\",\n",
-    "            negative=True,\n",
-    "        ),\n",
-    "        qo.AddColumn(\"stop\", \"start\", new_col_labels=\"los\", negative=True),\n",
-    "        qo.ConditionGreaterThan(\"los\", 1),\n",
-    "        qo.ConditionLessThan(\"los\", 21),\n",
-    "        qo.Keep(\n",
-    "            [\n",
-    "                \"encounter_id\",\n",
-    "                \"los\",\n",
-    "                \"age\",\n",
-    "                \"gender\",\n",
-    "            ],\n",
-    "        ),\n",
-    "    )\n",
-    "    return patient_encounters.ops(ops)\n",
-    "\n",
-    "\n",
-    "def get_observations(cohort):\n",
-    "    \"\"\"Get observations data.\"\"\"\n",
-    "    observations = querier.native.observations()\n",
-    "    ops = qo.Sequential(\n",
-    "        qo.ConditionIn(\n",
-    "            \"category\",\n",
-    "            [\n",
-    "                \"laboratory\",\n",
-    "                \"vital-signs\",\n",
-    "            ],\n",
-    "        ),\n",
-    "        qo.ConditionEquals(\"type\", \"numeric\"),\n",
-    "    )\n",
-    "    observations = observations.ops(ops)\n",
-    "    cohort = cohort.join(\n",
-    "        observations,\n",
-    "        on=(\"encounter_id\", \"encounter\"),\n",
-    "        isouter=True,\n",
-    "    )\n",
-    "    groupby_op = qo.GroupByAggregate(\n",
-    "        \"encounter_id\",\n",
-    "        {\"description\": (\"count\", \"n_obs\")},\n",
-    "    )\n",
-    "    observations = cohort.run()\n",
-    "    observations_count = cohort.ops(groupby_op).run()\n",
-    "    observations_stats = observations.pivot_table(\n",
-    "        index=\"encounter_id\",\n",
-    "        columns=\"description\",\n",
-    "        values=\"value\",\n",
-    "        aggfunc=\"max\",\n",
-    "    ).add_prefix(\"obs_\")\n",
-    "\n",
-    "    return [observations_count, observations_stats]\n",
-    "\n",
-    "\n",
-    "def get_medications(cohort):\n",
-    "    \"\"\"Get medications data.\"\"\"\n",
-    "    medications = querier.native.medications()\n",
-    "    cohort = cohort.join(\n",
-    "        medications,\n",
-    "        on=(\"encounter_id\", \"encounter\"),\n",
-    "    )\n",
-    "    groupby_op = qo.GroupByAggregate(\n",
-    "        \"encounter_id\",\n",
-    "        {\"description\": (\"count\", \"n_meds\")},\n",
-    "    )\n",
-    "\n",
-    "    return cohort.ops(groupby_op).run()\n",
-    "\n",
-    "\n",
-    "def get_procedures(cohort):\n",
-    "    \"\"\"Get procedures data.\"\"\"\n",
-    "    procedures = querier.native.procedures()\n",
-    "    cohort = cohort.join(\n",
-    "        procedures,\n",
-    "        on=(\"encounter_id\", \"encounter\"),\n",
-    "    )\n",
-    "    groupby_op = qo.GroupByAggregate(\n",
-    "        \"encounter_id\",\n",
-    "        {\"description\": (\"count\", \"n_procedures\")},\n",
-    "    )\n",
-    "\n",
-    "    return cohort.ops(groupby_op).run()\n",
-    "\n",
-    "\n",
-    "def run_query():\n",
-    "    \"\"\"Run query pipeline.\"\"\"\n",
-    "    cohort_query = get_encounters()\n",
-    "    to_merge = []\n",
-    "    observations = get_observations(cohort_query)\n",
-    "    to_merge.extend(observations)\n",
-    "    medications = get_medications(cohort_query)\n",
-    "    to_merge.append(medications)\n",
-    "    procedures = get_procedures(cohort_query)\n",
-    "    to_merge.append(procedures)\n",
-    "    cohort = cohort_query.run()\n",
-    "    for to_merge_df in to_merge:\n",
-    "        cohort = cohort.merge(\n",
-    "            to_merge_df,\n",
-    "            on=\"encounter_id\",\n",
-    "            how=\"left\",\n",
-    "        )\n",
-    "\n",
-    "    return cohort\n",
-    "\n",
-    "\n",
-    "cohort = run_query()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "389796a5-5abb-4c8f-bcb9-229d0e3e2108",
-   "metadata": {},
-   "source": [
-    "## Data Inspection and Preprocessing\n",
-    "\n",
-    "### Drop NaNs based on the `NAN_THRESHOLD`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c576ee51-e825-4970-86e8-3e5f221f145c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "null_counts = cohort.isnull().sum()[cohort.isnull().sum() > 0]\n",
-    "fig = go.Figure(data=[go.Bar(x=null_counts.index, y=null_counts.values)])\n",
-    "fig.update_layout(\n",
-    "    title=\"Number of Null Values per Column\",\n",
-    "    xaxis_title=\"Columns\",\n",
-    "    yaxis_title=\"Number of Null Values\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b77e5839-8c2f-4599-a511-2c3737e20772",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**\n",
-    "\n",
-    "We can use the log_plotly_figure method to add the figure to a section of the report. One can specify whether the figure should be interactive or not by setting the `interactive` parameter to `True` or `False` respectively. The default value is `True`. This\n",
-    "also affects the final size of the report. If the figure is interactive, the size of the report will be larger than if the figure is not interactive. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "190bcac3-2eda-4acc-bf1b-81cfd39a10b7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Number of Null Values per Column\",\n",
-    "    section_name=\"datasets\",\n",
-    "    interactive=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "32d67235-d839-4b70-bbd4-5126d8c6da49",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "thresh_nan = int(NAN_THRESHOLD * len(cohort))\n",
-    "cohort = cohort.dropna(axis=1, thresh=thresh_nan)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc5b45cb-2406-4330-b2fc-3b4823ff0c17",
-   "metadata": {},
-   "source": [
-    "### Length of stay distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f4cd2841-a992-4a89-936c-19bb90e6234d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "length_of_stay = cohort[\"los\"]\n",
-    "length_of_stay_counts = list(length_of_stay.value_counts().values)\n",
-    "length_of_stay_keys = list(length_of_stay.value_counts().keys())\n",
-    "cohort[\"outcome\"] = cohort[\"los\"] < NUM_DAYS\n",
-    "fig = go.Figure(data=[go.Bar(x=length_of_stay_keys, y=length_of_stay_counts)])\n",
-    "fig.update_layout(\n",
-    "    title=\"Length of stay\",\n",
-    "    xaxis_title=\"Days\",\n",
-    "    yaxis_title=\"Number of encounters\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc629738",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "57de20af",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Length of stay distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05156094-56e8-49c5-8e3c-478a1797db62",
-   "metadata": {},
-   "source": [
-    "### Outcome distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ae75c8a1-9316-4e47-ad45-7291a7b783bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cohort[\"outcome\"] = cohort[\"outcome\"].astype(\"int\")\n",
-    "fig = px.pie(cohort, names=\"outcome\")\n",
-    "fig.update_traces(textinfo=\"percent+label\")\n",
-    "fig.update_layout(title_text=\"Outcome Distribution\")\n",
-    "fig.update_traces(\n",
-    "    hovertemplate=\"Outcome: %{label}<br>Count: \\\n",
-    "    %{value}<br>Percent: %{percent}\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "742ba610-b754-458d-a77f-fc9469dcadab",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "794e085d-2dae-4dba-ade8-ab8cdc982af3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Outcome Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bab6a390-95f4-4876-a63d-210c95262960",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_counts = cohort[\"outcome\"].value_counts()\n",
-    "class_ratio = class_counts[0] / class_counts[1]\n",
-    "print(class_ratio)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e48376c2-a437-41f4-96fa-ea75f182f7b7",
-   "metadata": {},
-   "source": [
-    "### Gender distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d7ef4a36-3f9f-490a-befd-8e5fe41596ee",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.pie(cohort, names=\"gender\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Gender Distribution\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7b3673d-8df6-4aa8-9fff-f623f4d800ff",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8bd741fb-d0fe-4add-8d66-6b8baf271395",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Gender Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da0e832a-5763-42dc-a2e0-d91fef955ea5",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "###  Age distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ddb2b6cf-ee4d-4f83-8e56-6adcbe91f854",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(cohort, x=\"age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53519265-e826-459e-a6d6-de9c4cde41e7",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f3c4c351-8eb5-41cd-8a50-006cfcfb13c0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Age Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "483c9bb5-57bf-4a2c-960f-35f7e76eff1d",
-   "metadata": {},
-   "source": [
-    "### Identifying feature types\n",
-    "\n",
-    "Cyclops `TabularFeatures` class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2fb987af-f056-4886-84fa-4d4e1106e9b9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "features_list = [\n",
-    "    \"age\",\n",
-    "    \"gender\",\n",
-    "    \"n_obs\",\n",
-    "    \"n_meds\",\n",
-    "    \"n_procedures\",\n",
-    "    \"obs_Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma\",\n",
-    "    \"obs_Albumin [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma\",\n",
-    "    \"obs_Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma\",\n",
-    "    \"obs_Bilirubin.total [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Body Weight\",\n",
-    "    \"obs_Calcium [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Carbon dioxide  total [Moles/volume] in Serum or Plasma\",\n",
-    "    \"obs_Chloride [Moles/volume] in Serum or Plasma\",\n",
-    "    \"obs_Creatinine [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Diastolic Blood Pressure\",\n",
-    "    \"obs_Erythrocyte distribution width [Ratio] by Automated count\",\n",
-    "    \"obs_Erythrocytes [#/volume] in Blood by Automated count\",\n",
-    "    \"obs_Ferritin [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Glomerular filtration rate/1.73 sq M.predicted\",\n",
-    "    \"obs_Glucose [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Hematocrit [Volume Fraction] of Blood by Automated count\",\n",
-    "    \"obs_Hemoglobin [Mass/volume] in Blood\",\n",
-    "    \"obs_Leukocytes [#/volume] in Blood by Automated count\",\n",
-    "    \"obs_MCH [Entitic mass] by Automated count\",\n",
-    "    \"obs_MCHC [Mass/volume] by Automated count\",\n",
-    "    \"obs_MCV [Entitic volume] by Automated count\",\n",
-    "    \"obs_Oxygen saturation in Arterial blood\",\n",
-    "    \"obs_Platelets [#/volume] in Blood by Automated count\",\n",
-    "    \"obs_Potassium [Moles/volume] in Serum or Plasma\",\n",
-    "    \"obs_Protein [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Sodium [Moles/volume] in Serum or Plasma\",\n",
-    "    \"obs_Systolic Blood Pressure\",\n",
-    "    \"obs_Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method\",  # noqa: E501\n",
-    "    \"obs_Urea nitrogen [Mass/volume] in Serum or Plasma\",\n",
-    "]\n",
-    "features_list = sorted(features_list)\n",
-    "tab_features = TabularFeatures(\n",
-    "    data=cohort.reset_index(),\n",
-    "    features=features_list,\n",
-    "    by=\"encounter_id\",\n",
-    "    targets=\"outcome\",\n",
-    ")\n",
-    "print(tab_features.types)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2738074-00be-46fa-999f-77f85add9469",
-   "metadata": {},
-   "source": [
-    "### Creating data preprocessors\n",
-    "\n",
-    "We create a data preprocessor using sklearn's ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2bb80cfb-2e48-4ecf-a325-0026dad4aef7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"mean\")), (\"scaler\", MinMaxScaler())],\n",
-    ")\n",
-    "\n",
-    "binary_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"most_frequent\"))],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "31ec3441-7dba-4b9a-9256-ad6b0293829d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_features = sorted((tab_features.features_by_type(\"numeric\")))\n",
-    "numeric_indices = [\n",
-    "    cohort[features_list].columns.get_loc(column) for column in numeric_features\n",
-    "]\n",
-    "print(numeric_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f2fb5345-17f9-44ac-b944-079324a098e4",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "binary_features = sorted(tab_features.features_by_type(\"binary\"))\n",
-    "binary_features.remove(\"outcome\")\n",
-    "binary_indices = [\n",
-    "    cohort[features_list].columns.get_loc(column) for column in binary_features\n",
-    "]\n",
-    "print(binary_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "868fa57c-f12a-4821-a85e-31c6c95d6e76",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "preprocessor = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        (\"num\", numeric_transformer, numeric_indices),\n",
-    "        (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"), binary_indices),\n",
-    "    ],\n",
-    "    remainder=\"passthrough\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a178f34-3883-43c2-8009-28063619df2c",
-   "metadata": {},
-   "source": [
-    "## Creating Hugging Face Dataset\n",
-    "\n",
-    "We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "87b72a66-fc46-4e7a-a612-1f66de811c0d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "cohort = cohort.drop(columns=[\"encounter_id\", \"los\"])\n",
-    "dataset = Dataset.from_pandas(cohort)\n",
-    "dataset.cleanup_cache_files()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c6370e7a-c559-431f-9ebc-0b35bcdfb029",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = dataset.cast_column(\"outcome\", ClassLabel(num_classes=2))\n",
-    "dataset = dataset.train_test_split(\n",
-    "    train_size=TRAIN_SIZE,\n",
-    "    stratify_by_column=\"outcome\",\n",
-    "    seed=RANDOM_SEED,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71007875-2e2c-460f-a956-bcd419cb2017",
-   "metadata": {},
-   "source": [
-    "## Model Creation\n",
-    "\n",
-    "CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a XGBoost classifier to fit a logisitic regression model. The model configurations can be passed to `create_model` based on the parameters for XGBClassifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "42e8d147-545c-47a7-9a91-b06b27a37d47",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_name = \"xgb_classifier\"\n",
-    "model = create_model(model_name, random_state=123)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff3ae174-59c0-41df-954c-f6e05f64c8dd",
-   "metadata": {},
-   "source": [
-    "## Task Creation\n",
-    "\n",
-    "We use Cyclops tasks to define our model's task (in this case, BinaryTabularClassificationTask), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "01d36851-292a-4849-98e0-9183c2dec87a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "los_task = BinaryTabularClassificationTask(\n",
-    "    {model_name: model},\n",
-    "    task_features=features_list,\n",
-    "    task_target=\"outcome\",\n",
-    ")\n",
-    "los_task.list_models()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cfb95f1e-ae78-469c-88d8-750d9c36a349",
-   "metadata": {},
-   "source": [
-    "## Training\n",
-    "\n",
-    "If `best_model_params` is passed to the `train` method, the best model will be selected after the hyperparameter search. The parameters in `best_model_params` indicate the values to create the parameters grid.\n",
-    "\n",
-    "Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "572b0fe0-5891-4aca-9e63-8d556851caec",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "best_model_params = {\n",
-    "    \"n_estimators\": [100, 250, 500],\n",
-    "    \"learning_rate\": [0.1, 0.01],\n",
-    "    \"max_depth\": [2, 5],\n",
-    "    \"reg_lambda\": [0, 1, 10],\n",
-    "    \"colsample_bytree\": [0.7, 0.8, 1],\n",
-    "    \"gamma\": [0, 1, 2, 10],\n",
-    "    \"method\": \"random\",\n",
-    "}\n",
-    "los_task.train(\n",
-    "    dataset[\"train\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    best_model_params=best_model_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9b687a58-a6e5-46f7-bd9e-106376912086",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_params = los_task.list_models_params()[model_name]\n",
-    "print(model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e699187-2650-41ab-aba8-42bf9c635819",
-   "metadata": {},
-   "source": [
-    "**Log the model parameters to the report.**\n",
-    "\n",
-    "We can add model parameters to the model card using the `log_model_parameters` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4cfdfe29-c813-45cd-9ae7-2959daa0cd8f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_model_parameters(params=model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d772526e-f595-4f4a-8026-7ab39523b3df",
-   "metadata": {},
-   "source": [
-    "## Prediction\n",
-    "\n",
-    "The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c3fee35c-902e-4c54-89b7-c42c1e935e1b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "y_pred = los_task.predict(\n",
-    "    dataset[\"test\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    proba=False,\n",
-    "    only_predictions=True,\n",
-    ")\n",
-    "print(len(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2eb06bb-9c85-4597-a07d-f3f16c841c56",
-   "metadata": {},
-   "source": [
-    "## Evaluation\n",
-    "\n",
-    "Evaluation is done using various evaluation metrics that provide different perspectives on the model's predictive abilities i.e. standard performance metrics and fairness metrics.\n",
-    "\n",
-    "The standard performance metrics can be created using the `MetricDict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d3d5b101-17a8-4610-ae17-e04cf962d2d1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_auroc\",\n",
-    "    \"binary_roc_curve\",\n",
-    "    \"binary_precision_recall_curve\",\n",
-    "    \"binary_confusion_matrix\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8cdf9238-dfc8-4eb2-a5c1-e0d273c75ee3",
-   "metadata": {},
-   "source": [
-    "In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using `SliceSpec` objects. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "06d72639-db3b-4e91-be9d-3bf9dbb0f0e8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"min_value\": 20,\n",
-    "            \"max_value\": 50,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"min_value\": 50,\n",
-    "            \"max_value\": 80,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\"gender\": {\"value\": \"M\"}},\n",
-    "    {\"gender\": {\"value\": \"F\"}},\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67bd7806-c480-4c47-8e33-6612c2ede93e",
-   "metadata": {},
-   "source": [
-    "A `MetricDict` can also be defined for the fairness metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "24877057-f737-43bf-a61d-3dd4e45e0381",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "specificity = create_metric(metric_name=\"binary_specificity\", experimental=True)\n",
-    "sensitivity = create_metric(metric_name=\"binary_sensitivity\", experimental=True)\n",
-    "fpr = (\n",
-    "    -specificity + 1\n",
-    ")  # rsub is not supported due to limitations in the array API standard\n",
-    "fnr = -sensitivity + 1\n",
-    "ber = (fpr + fnr) / 2\n",
-    "fairness_metric_collection = MetricDict(\n",
-    "    {\n",
-    "        \"Sensitivity\": sensitivity,\n",
-    "        \"Specificity\": specificity,\n",
-    "        \"BER\": ber,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b7f01c5-9598-4dcb-8697-ebc03a060d1c",
-   "metadata": {},
-   "source": [
-    "The FairnessConfig helps in setting up and evaluating the fairness of the model predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "945fdb5c-1b55-42f8-9644-e2bf6efd7011",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fairness_config = FairnessConfig(\n",
-    "    metrics=fairness_metric_collection,\n",
-    "    dataset=None,  # dataset is passed from the evaluator\n",
-    "    target_columns=None,  # target columns are passed from the evaluator\n",
-    "    groups=[\"gender\", \"age\"],\n",
-    "    group_bins={\"age\": [20, 40]},\n",
-    "    group_base_values={\"age\": 40, \"gender\": \"M\"},\n",
-    "    thresholds=[0.5],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e7d0840-f686-40c5-bb35-2ea37734553c",
-   "metadata": {},
-   "source": [
-    "The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3797fe8c-886e-426f-8ea9-ad5afad3b65b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "results, dataset_with_preds = los_task.evaluate(\n",
-    "    dataset[\"test\"],\n",
-    "    metric_collection,\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=slice_spec,\n",
-    "    batch_size=-1,\n",
-    "    fairness_config=fairness_config,\n",
-    "    override_fairness_metrics=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d2d1d75-f7d8-44d3-a782-2aba9a4fbac0",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "**Log the performance metrics to the report.**\n",
-    "\n",
-    "We can add a performance metric to the model card using the `log_performance_metric` method, which expects a dictionary where the keys are in the following format: `slice_name/metric_name`. For instance, `overall/accuracy`. \n",
-    "\n",
-    "We first need to process the evaluation results to get the metrics in the right format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d322a86f-1f7c-42f6-8a97-8a18ea8622e2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_name = f\"model_for_preds.{model_name}\"\n",
-    "results_flat = flatten_results_dict(\n",
-    "    results=results,\n",
-    "    remove_metrics=[\"BinaryROC\", \"BinaryPrecisionRecallCurve\"],\n",
-    "    model_name=model_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d33a171c-02ef-4bc9-a3bf-87320c7c83d6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    if name == \"BinaryConfusionMatrix\":\n",
-    "        continue\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ee464d3-7246-4c6f-ac2d-8cca2a985f22",
-   "metadata": {},
-   "source": [
-    "We can also use the `ClassificationPlotter` to plot the performance metrics and the add the figure to the model card using the `log_plotly_figure` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c1acc80b-9dce-4f10-9641-dba86cae0a50",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba3a525b-63d5-4d35-b248-8fa67dc5a2c2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# extracting the ROC curves and AUROC results for all the slices\n",
-    "roc_curves = {\n",
-    "    slice_name: slice_results[\"BinaryROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "aurocs = {\n",
-    "    slice_name: slice_results[\"BinaryAUROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "roc_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba032f62",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plot confusion matrix\n",
-    "confusion_matrix = results[model_name][\"overall\"][\"BinaryConfusionMatrix\"]\n",
-    "conf_plot = plotter.confusion_matrix(\n",
-    "    confusion_matrix,\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=conf_plot,\n",
-    "    caption=\"Confusion Matrix\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "conf_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9774f0ef-89a9-4c65-9021-635a90122676",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# plotting the ROC curves for all the slices\n",
-    "roc_plot = plotter.roc_curve_comparison(roc_curves, aurocs=aurocs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=roc_plot,\n",
-    "    caption=\"ROC Curve for Female Patients\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "roc_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6d31a984-bd7f-419d-95dd-be4f397e853a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the overall classification metric values.\n",
-    "overall_performance = {\n",
-    "    metric_name: metric_value\n",
-    "    for metric_name, metric_value in results[model_name][\"overall\"].items()\n",
-    "    if metric_name\n",
-    "    not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\", \"BinaryConfusionMatrix\"]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a264f7a3-db58-4cbf-a67b-63a9ffe46b98",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the overall classification metric values.\n",
-    "overall_performance_plot = plotter.metrics_value(\n",
-    "    overall_performance,\n",
-    "    title=\"Overall Performance\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=overall_performance_plot,\n",
-    "    caption=\"Overall Performance\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "overall_performance_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a463b995-e2a3-4e2e-8339-7caa5d885482",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name\n",
-    "        not in [\"BinaryROCCurve\", \"BinaryPrecisionRecallCurve\", \"BinaryConfusionMatrix\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a2f5b559-ec42-47f8-bf73-4326e595271b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=slice_metrics_plot,\n",
-    "    caption=\"Slice Metric Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "22439e46-02a1-4ec6-83d5-9ab5ed9e57b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reformatting the fairness metrics\n",
-    "fairness_results = copy.deepcopy(results[\"fairness\"])\n",
-    "fairness_metrics = {}\n",
-    "# remove the group size from the fairness results and add it to the slice name\n",
-    "for slice_name, slice_results in fairness_results.items():\n",
-    "    group_size = slice_results.pop(\"Group Size\")\n",
-    "    fairness_metrics[f\"{slice_name} (Size={group_size})\"] = slice_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b82589f2-46e2-4936-871b-f26535a3e0c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the fairness metrics\n",
-    "fairness_plot = plotter.metrics_comparison_scatter(\n",
-    "    fairness_metrics,\n",
-    "    title=\"Fairness Metrics\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fairness_plot,\n",
-    "    caption=\"Fairness Metrics\",\n",
-    "    section_name=\"fairness analysis\",\n",
-    ")\n",
-    "fairness_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be00d959-442d-41f8-92fc-60abedef133a",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Report Generation\n",
-    "\n",
-    "Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.\n",
-    "\n",
-    "\n",
-    "Let's start with populating the model details section, which includes the following fields by default:\n",
-    "- description: A high-level description of the model and its usage for a general audience.\n",
-    "- version: The version of the model.\n",
-    "- owners: The individuals or organizations that own the model.\n",
-    "- license: The license under which the model is made available.\n",
-    "- citation: The citation for the model.\n",
-    "- references: Links to resources that are relevant to the model.\n",
-    "- path: The path to where the model is stored.\n",
-    "- regulatory_requirements: The regulatory requirements that are relevant to the model.\n",
-    "\n",
-    "We can add additional fields to the model details section by passing a dictionary to the `log_from_dict` method and specifying the section name as `model_details`. You can also use the `log_descriptor` method to add a new field object with a `description` attribute to any section of the model card."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5c68db3-e18b-43c1-b37e-ec6018cc03c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"Prolonged Length of Stay Prediction Model\",\n",
-    "        \"description\": \"The model was trained on the Synthea synthetic dataset \\\n",
-    "            to predict prolonged stay in the hospital.\",\n",
-    "    },\n",
-    "    section_name=\"model_details\",\n",
-    ")\n",
-    "report.log_version(\n",
-    "    version_str=\"0.0.1\",\n",
-    "    date=str(date.today()),\n",
-    "    description=\"Initial Release\",\n",
-    ")\n",
-    "report.log_owner(\n",
-    "    name=\"CyclOps Team\",\n",
-    "    contact=\"vectorinstitute.github.io/cyclops/\",\n",
-    "    email=\"cyclops@vectorinstitute.ai\",\n",
-    ")\n",
-    "report.log_license(identifier=\"Apache-2.0\")\n",
-    "report.log_reference(\n",
-    "    link=\"https://xgboost.readthedocs.io/en/stable/python/python_api.html\",  # noqa: E501\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3cf7f12-db00-47b7-abb0-5259bce56500",
-   "metadata": {},
-   "source": [
-    "Next, let's populate the considerations section, which includes the following fields by default:\n",
-    "- users: The intended users of the model.\n",
-    "- use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases.\n",
-    "- fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms.\n",
-    "- ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This can be populated using the  `log_risk` method.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4825be83-b601-4daa-94a8-c7819f53b22b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"users\": [\n",
-    "            {\"description\": \"Hospitals\"},\n",
-    "            {\"description\": \"Clinicians\"},\n",
-    "        ],\n",
-    "    },\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_user(description=\"ML Engineers\")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting prolonged length of stay\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"sex, age\",\n",
-    "    benefit=\"Improved health outcomes for patients.\",\n",
-    "    harm=\"Biased predictions for patients in certain groups (e.g. older patients) \\\n",
-    "        may lead to worse health outcomes.\",\n",
-    "    mitigation_strategy=\"We will monitor the performance of the model on these groups \\\n",
-    "        and retrain the model if the performance drops below a certain threshold.\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"The model may be used to make decisions that affect the health of patients.\",\n",
-    "    mitigation_strategy=\"The model should be continuously monitored for performance \\\n",
-    "        and retrained if the performance drops below a certain threshold.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fea2ac13-7185-4327-9eef-e4cf08f3b3dd",
-   "metadata": {},
-   "source": [
-    "Once the model card is populated, you can generate the report using the `export` method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named `cyclops_reports` in the current working directory. You can change the path by passing a `output_dir` argument when instantiating the `ModelCardReport` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "257043a9-ae7b-4db6-a2f5-b03ff57320a7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "synthetic_timestamps = [\n",
-    "    \"2021-09-01\",\n",
-    "    \"2021-10-01\",\n",
-    "    \"2021-11-01\",\n",
-    "    \"2021-12-01\",\n",
-    "    \"2022-01-01\",\n",
-    "]\n",
-    "report._model_card.overview = None\n",
-    "report_path = report.export(\n",
-    "    output_filename=\"length_of_stay_report_periodic.html\",\n",
-    "    synthetic_timestamp=synthetic_timestamps[0],\n",
-    ")\n",
-    "shutil.copy(f\"{report_path}\", \".\")\n",
-    "for i in range(4):\n",
-    "    report._model_card.overview = None\n",
-    "    for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "        metric.value = np.clip(\n",
-    "            metric.value + np.random.normal(0, 0.1),\n",
-    "            0,\n",
-    "            1,\n",
-    "        )\n",
-    "        metric.tests[0].passed = bool(metric.value >= 0.7)\n",
-    "    report_path = report.export(\n",
-    "        output_filename=\"length_of_stay_report_periodic.html\",\n",
-    "        synthetic_timestamp=synthetic_timestamps[i + 1],\n",
-    "    )\n",
-    "    shutil.copy(f\"{report_path}\", \".\")\n",
-    "shutil.rmtree(\"./cyclops_report\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d953f1b-845d-424e-b8b6-d782973d9e84",
-   "metadata": {},
-   "source": [
-    "You can view the generated HTML [report](./length_of_stay_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/api/_static/nbsphinx-broken-thumbnail.svg b/api/_static/nbsphinx-broken-thumbnail.svg
deleted file mode 100644
index 4919ca882..000000000
--- a/api/_static/nbsphinx-broken-thumbnail.svg
+++ /dev/null
@@ -1,9 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
-  <style>
-    svg { fill: lightcoral; }
-    @media (prefers-color-scheme: dark) {
-      svg { fill: crimson; }
-    }
-  </style>
-  <path d="m 80.856887,38.34474 h 2.903414 l 7.066397,13.332221 V 38.34474 h 2.092166 V 54.281494 H 90.01545 L 82.949053,40.949273 v 13.332221 h -2.092166 z m -13.812565,0 h 10.076555 v 1.814633 h -7.920343 v 4.718048 h 7.589439 v 1.814634 h -7.589439 v 5.774805 h 8.11248 v 1.814634 H 67.044322 Z m -14.335606,0 h 2.156212 v 6.735493 L 62.01672,38.34474 h 2.775323 l -7.909669,7.429324 8.475407,8.50743 h -2.839368 l -7.653485,-7.674833 v 7.674833 h -2.156212 z m -10.738365,1.462381 q -2.348349,0 -3.73601,1.750588 -1.376987,1.750588 -1.376987,4.771419 0,3.010158 1.376987,4.760746 1.387661,1.750587 3.73601,1.750587 2.34835,0 3.714663,-1.750587 1.376986,-1.750588 1.376986,-4.760746 0,-3.020831 -1.376986,-4.771419 -1.366313,-1.750588 -3.714663,-1.750588 z m 0,-1.750588 q 3.351736,0 5.358507,2.252281 2.006772,2.241606 2.006772,6.020314 0,3.768034 -2.006772,6.020315 -2.006771,2.241606 -5.358507,2.241606 -3.362409,0 -5.379855,-2.241606 -2.006772,-2.241607 -2.006772,-6.020315 0,-3.778708 2.006772,-6.020314 2.017446,-2.252281 5.379855,-2.252281 z m -14.100771,8.75294 q 0.693831,0.234835 1.344964,1.003385 0.661808,0.768551 1.323615,2.113515 l 2.188235,4.355121 H 30.410068 L 28.371273,50.193231 Q 27.581374,48.592083 26.834172,48.069042 26.097644,47.546 24.816726,47.546 h -2.34835 v 6.735494 H 20.312164 V 38.34474 h 4.867489 q 2.732625,0 4.077588,1.142152 1.344964,1.142151 1.344964,3.447804 0,1.505078 -0.704505,2.49779 -0.69383,0.992711 -2.02812,1.376987 z m -5.401204,-6.692797 v 5.657388 h 2.711277 q 1.55845,0 2.348349,-0.715179 0.800574,-0.725854 0.800574,-2.124189 0,-1.398336 -0.800574,-2.102841 -0.789899,-0.715179 -2.348349,-0.715179 z M 7.4709616,46.670707 v 5.838851 H 10.92944 q 1.739914,0 2.57251,-0.71518 0.843271,-0.725853 0.843271,-2.209583 0,-1.494404 -0.843271,-2.198909 -0.832596,-0.715179 -2.57251,-0.715179 z m 0,-6.554031 v 4.803443 h 3.1916204 q 1.579799,0 2.34835,-0.587088 0.779225,-0.597761 0.779225,-1.814634 0,-1.206197 -0.779225,-1.803959 -0.768551,-0.597762 -2.34835,-0.597762 z M 5.3147497,38.34474 h 5.5079473 q 2.465767,0 3.800057,1.024734 1.334289,1.024734 1.334289,2.914088 0,1.462382 -0.683156,2.327001 -0.683156,0.86462 -2.006772,1.078106 1.590474,0.341579 2.465768,1.430359 0.885968,1.078106 0.885968,2.700602 0,2.134863 -1.451707,3.298364 -1.451707,1.1635 -4.130961,1.1635 H 5.3147497 Z M 65.365328,74.550032 H 67.52154 V 88.67217 h 7.760238 v 1.814634 h -9.91645 z m -6.447298,0 h 2.156214 V 90.486804 H 58.91803 Z m -9.955303,2.124192 -2.924765,7.931025 h 5.860208 z m -1.216872,-2.124192 h 2.444423 l 6.073692,15.936772 h -2.241606 l -1.451709,-4.088268 h -7.183824 l -1.451707,4.088268 H 41.66149 Z m -21.264514,0 h 2.903417 L 36.45116,87.882271 V 74.550032 h 2.092171 V 90.486804 H 35.639914 L 28.573506,77.154569 v 13.332235 h -2.092165 z m 53.454813,-9.781663 v 5.838858 h 3.458479 q 1.739919,0 2.572513,-0.71518 0.843272,-0.725854 0.843272,-2.209586 0,-1.494406 -0.843272,-2.198912 -0.832594,-0.71518 -2.572513,-0.71518 z m 0,-6.554037 v 4.803447 h 3.191622 q 1.579803,0 2.348355,-0.587087 0.779225,-0.597763 0.779225,-1.814636 0,-1.206199 -0.779225,-1.803962 -0.768552,-0.597762 -2.348355,-0.597762 z m -2.156212,-1.771939 h 5.507949 q 2.465772,0 3.800065,1.024736 1.334288,1.024735 1.334288,2.914092 0,1.462383 -0.683157,2.327003 -0.683157,0.864621 -2.006771,1.078107 1.590471,0.341579 2.46577,1.43036 0.885967,1.078107 0.885967,2.700605 0,2.134866 -1.451708,3.298368 -1.451709,1.163502 -4.130963,1.163502 h -5.72144 z m -18.86154,0 h 3.212969 l 4.066921,10.845118 4.088269,-10.845118 H 73.49953 V 72.379166 H 71.396692 V 58.385121 L 67.287075,69.315634 H 65.120183 L 61.010567,58.385121 v 13.994045 h -2.092165 z m -16.246333,0 h 2.166892 v 9.681616 q 0,2.561839 0.928668,3.693318 0.928667,1.120804 3.010159,1.120804 2.070819,0 2.999486,-1.120804 0.928667,-1.131479 0.928667,-3.693318 v -9.681616 h 2.166886 v 9.948474 q 0,3.116904 -1.547776,4.70738 -1.537104,1.590474 -4.547263,1.590474 -3.020833,0 -4.568615,-1.590474 -1.537104,-1.590476 -1.537104,-4.70738 z m -16.192953,0 h 2.156213 v 6.532689 h 7.83496 v -6.532689 h 2.156213 v 15.936773 h -2.156213 v -7.589448 h -7.83496 v 7.589448 h -2.156213 z m -15.563168,0 h 13.481671 v 1.814636 H 18.740226 V 72.379166 H 16.573339 V 58.257029 h -5.657391 z" />
-</svg>
diff --git a/api/_static/nbsphinx-code-cells.css b/api/_static/nbsphinx-code-cells.css
deleted file mode 100644
index a3fb27c30..000000000
--- a/api/_static/nbsphinx-code-cells.css
+++ /dev/null
@@ -1,259 +0,0 @@
-/* remove conflicting styling from Sphinx themes */
-div.nbinput.container div.prompt *,
-div.nboutput.container div.prompt *,
-div.nbinput.container div.input_area pre,
-div.nboutput.container div.output_area pre,
-div.nbinput.container div.input_area .highlight,
-div.nboutput.container div.output_area .highlight {
-    border: none;
-    padding: 0;
-    margin: 0;
-    box-shadow: none;
-}
-
-div.nbinput.container > div[class*=highlight],
-div.nboutput.container > div[class*=highlight] {
-    margin: 0;
-}
-
-div.nbinput.container div.prompt *,
-div.nboutput.container div.prompt * {
-    background: none;
-}
-
-div.nboutput.container div.output_area .highlight,
-div.nboutput.container div.output_area pre {
-    background: unset;
-}
-
-div.nboutput.container div.output_area div.highlight {
-    color: unset;  /* override Pygments text color */
-}
-
-/* avoid gaps between output lines */
-div.nboutput.container div[class*=highlight] pre {
-    line-height: normal;
-}
-
-/* input/output containers */
-div.nbinput.container,
-div.nboutput.container {
-    display: -webkit-flex;
-    display: flex;
-    align-items: flex-start;
-    margin: 0;
-    width: 100%;
-}
-@media (max-width: 540px) {
-    div.nbinput.container,
-    div.nboutput.container {
-        flex-direction: column;
-    }
-}
-
-/* input container */
-div.nbinput.container {
-    padding-top: 5px;
-}
-
-/* last container */
-div.nblast.container {
-    padding-bottom: 5px;
-}
-
-/* input prompt */
-div.nbinput.container div.prompt pre,
-/* for sphinx_immaterial theme: */
-div.nbinput.container div.prompt pre > code {
-    color: #307FC1;
-}
-
-/* output prompt */
-div.nboutput.container div.prompt pre,
-/* for sphinx_immaterial theme: */
-div.nboutput.container div.prompt pre > code {
-    color: #BF5B3D;
-}
-
-/* all prompts */
-div.nbinput.container div.prompt,
-div.nboutput.container div.prompt {
-    width: 4.5ex;
-    padding-top: 5px;
-    position: relative;
-    user-select: none;
-}
-
-div.nbinput.container div.prompt > div,
-div.nboutput.container div.prompt > div {
-    position: absolute;
-    right: 0;
-    margin-right: 0.3ex;
-}
-
-@media (max-width: 540px) {
-    div.nbinput.container div.prompt,
-    div.nboutput.container div.prompt {
-        width: unset;
-        text-align: left;
-        padding: 0.4em;
-    }
-    div.nboutput.container div.prompt.empty {
-        padding: 0;
-    }
-
-    div.nbinput.container div.prompt > div,
-    div.nboutput.container div.prompt > div {
-        position: unset;
-    }
-}
-
-/* disable scrollbars and line breaks on prompts */
-div.nbinput.container div.prompt pre,
-div.nboutput.container div.prompt pre {
-    overflow: hidden;
-    white-space: pre;
-}
-
-/* input/output area */
-div.nbinput.container div.input_area,
-div.nboutput.container div.output_area {
-    -webkit-flex: 1;
-    flex: 1;
-    overflow: auto;
-}
-@media (max-width: 540px) {
-    div.nbinput.container div.input_area,
-    div.nboutput.container div.output_area {
-        width: 100%;
-    }
-}
-
-/* input area */
-div.nbinput.container div.input_area {
-    border: 1px solid #e0e0e0;
-    border-radius: 2px;
-    /*background: #f5f5f5;*/
-}
-
-/* override MathJax center alignment in output cells */
-div.nboutput.container div[class*=MathJax] {
-    text-align: left !important;
-}
-
-/* override sphinx.ext.imgmath center alignment in output cells */
-div.nboutput.container div.math p {
-    text-align: left;
-}
-
-/* standard error */
-div.nboutput.container div.output_area.stderr {
-    background: #fdd;
-}
-
-/* ANSI colors */
-.ansi-black-fg { color: #3E424D; }
-.ansi-black-bg { background-color: #3E424D; }
-.ansi-black-intense-fg { color: #282C36; }
-.ansi-black-intense-bg { background-color: #282C36; }
-.ansi-red-fg { color: #E75C58; }
-.ansi-red-bg { background-color: #E75C58; }
-.ansi-red-intense-fg { color: #B22B31; }
-.ansi-red-intense-bg { background-color: #B22B31; }
-.ansi-green-fg { color: #00A250; }
-.ansi-green-bg { background-color: #00A250; }
-.ansi-green-intense-fg { color: #007427; }
-.ansi-green-intense-bg { background-color: #007427; }
-.ansi-yellow-fg { color: #DDB62B; }
-.ansi-yellow-bg { background-color: #DDB62B; }
-.ansi-yellow-intense-fg { color: #B27D12; }
-.ansi-yellow-intense-bg { background-color: #B27D12; }
-.ansi-blue-fg { color: #208FFB; }
-.ansi-blue-bg { background-color: #208FFB; }
-.ansi-blue-intense-fg { color: #0065CA; }
-.ansi-blue-intense-bg { background-color: #0065CA; }
-.ansi-magenta-fg { color: #D160C4; }
-.ansi-magenta-bg { background-color: #D160C4; }
-.ansi-magenta-intense-fg { color: #A03196; }
-.ansi-magenta-intense-bg { background-color: #A03196; }
-.ansi-cyan-fg { color: #60C6C8; }
-.ansi-cyan-bg { background-color: #60C6C8; }
-.ansi-cyan-intense-fg { color: #258F8F; }
-.ansi-cyan-intense-bg { background-color: #258F8F; }
-.ansi-white-fg { color: #C5C1B4; }
-.ansi-white-bg { background-color: #C5C1B4; }
-.ansi-white-intense-fg { color: #A1A6B2; }
-.ansi-white-intense-bg { background-color: #A1A6B2; }
-
-.ansi-default-inverse-fg { color: #FFFFFF; }
-.ansi-default-inverse-bg { background-color: #000000; }
-
-.ansi-bold { font-weight: bold; }
-.ansi-underline { text-decoration: underline; }
-
-
-div.nbinput.container div.input_area div[class*=highlight] > pre,
-div.nboutput.container div.output_area div[class*=highlight] > pre,
-div.nboutput.container div.output_area div[class*=highlight].math,
-div.nboutput.container div.output_area.rendered_html,
-div.nboutput.container div.output_area > div.output_javascript,
-div.nboutput.container div.output_area:not(.rendered_html) > img{
-    padding: 5px;
-    margin: 0;
-}
-
-/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
-div.nbinput.container div.input_area > div[class^='highlight'],
-div.nboutput.container div.output_area > div[class^='highlight']{
-    overflow-y: hidden;
-}
-
-/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
-.prompt .copybtn,
-/* ... and 'sphinx_immaterial' theme */
-.prompt .md-clipboard.md-icon {
-    display: none;
-}
-
-/* Some additional styling taken form the Jupyter notebook CSS */
-.jp-RenderedHTMLCommon table,
-div.rendered_html table {
-  border: none;
-  border-collapse: collapse;
-  border-spacing: 0;
-  color: black;
-  font-size: 12px;
-  table-layout: fixed;
-}
-.jp-RenderedHTMLCommon thead,
-div.rendered_html thead {
-  border-bottom: 1px solid black;
-  vertical-align: bottom;
-}
-.jp-RenderedHTMLCommon tr,
-.jp-RenderedHTMLCommon th,
-.jp-RenderedHTMLCommon td,
-div.rendered_html tr,
-div.rendered_html th,
-div.rendered_html td {
-  text-align: right;
-  vertical-align: middle;
-  padding: 0.5em 0.5em;
-  line-height: normal;
-  white-space: normal;
-  max-width: none;
-  border: none;
-}
-.jp-RenderedHTMLCommon th,
-div.rendered_html th {
-  font-weight: bold;
-}
-.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
-div.rendered_html tbody tr:nth-child(odd) {
-  background: #f5f5f5;
-}
-.jp-RenderedHTMLCommon tbody tr:hover,
-div.rendered_html tbody tr:hover {
-  background: rgba(66, 165, 245, 0.2);
-}
-
diff --git a/api/_static/nbsphinx-gallery.css b/api/_static/nbsphinx-gallery.css
deleted file mode 100644
index 365c27a96..000000000
--- a/api/_static/nbsphinx-gallery.css
+++ /dev/null
@@ -1,31 +0,0 @@
-.nbsphinx-gallery {
-    display: grid;
-    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
-    gap: 5px;
-    margin-top: 1em;
-    margin-bottom: 1em;
-}
-
-.nbsphinx-gallery > a {
-    padding: 5px;
-    border: 1px dotted currentColor;
-    border-radius: 2px;
-    text-align: center;
-}
-
-.nbsphinx-gallery > a:hover {
-    border-style: solid;
-}
-
-.nbsphinx-gallery img {
-    max-width: 100%;
-    max-height: 100%;
-}
-
-.nbsphinx-gallery > a > div:first-child {
-    display: flex;
-    align-items: start;
-    justify-content: center;
-    height: 120px;
-    margin-bottom: 5px;
-}
diff --git a/api/_static/nbsphinx-no-thumbnail.svg b/api/_static/nbsphinx-no-thumbnail.svg
deleted file mode 100644
index 9dca7588f..000000000
--- a/api/_static/nbsphinx-no-thumbnail.svg
+++ /dev/null
@@ -1,9 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
-  <style>
-    svg { fill: #ccc; }
-    @media (prefers-color-scheme: dark) {
-      svg { fill: #999; }
-    }
-  </style>
-  <path d="M 65.365328,74.550032 H 67.52154 V 88.67217 h 7.760238 v 1.814634 h -9.91645 z m -6.447298,0 h 2.156214 V 90.486804 H 58.91803 Z m -9.955303,2.124192 -2.924765,7.931025 h 5.860208 z m -1.216872,-2.124192 h 2.444423 l 6.073692,15.936772 h -2.241606 l -1.451709,-4.088268 h -7.183824 l -1.451707,4.088268 H 41.66149 Z m -21.264514,0 h 2.903417 L 36.45116,87.882271 V 74.550032 h 2.092171 V 90.486804 H 35.639914 L 28.573506,77.154569 v 13.332235 h -2.092165 z m 53.454813,-9.781663 v 5.838858 h 3.458479 q 1.739919,0 2.572513,-0.71518 0.843272,-0.725854 0.843272,-2.209586 0,-1.494406 -0.843272,-2.198912 -0.832594,-0.71518 -2.572513,-0.71518 z m 0,-6.554037 v 4.803447 h 3.191622 q 1.579803,0 2.348355,-0.587087 0.779225,-0.597763 0.779225,-1.814636 0,-1.206199 -0.779225,-1.803962 -0.768552,-0.597762 -2.348355,-0.597762 z m -2.156212,-1.771939 h 5.507949 q 2.465772,0 3.800065,1.024736 1.334288,1.024735 1.334288,2.914092 0,1.462383 -0.683157,2.327003 -0.683157,0.864621 -2.006771,1.078107 1.590471,0.341579 2.46577,1.43036 0.885967,1.078107 0.885967,2.700605 0,2.134866 -1.451708,3.298368 -1.451709,1.163502 -4.130963,1.163502 h -5.72144 z m -18.86154,0 h 3.212969 l 4.066921,10.845118 4.088269,-10.845118 H 73.49953 V 72.379166 H 71.396692 V 58.385121 L 67.287075,69.315634 H 65.120183 L 61.010567,58.385121 v 13.994045 h -2.092165 z m -16.246333,0 h 2.166892 v 9.681616 q 0,2.561839 0.928668,3.693318 0.928667,1.120804 3.010159,1.120804 2.070819,0 2.999486,-1.120804 0.928667,-1.131479 0.928667,-3.693318 v -9.681616 h 2.166886 v 9.948474 q 0,3.116904 -1.547776,4.70738 -1.537104,1.590474 -4.547263,1.590474 -3.020833,0 -4.568615,-1.590474 -1.537104,-1.590476 -1.537104,-4.70738 z m -16.192953,0 h 2.156213 v 6.532689 h 7.83496 v -6.532689 h 2.156213 v 15.936773 h -2.156213 v -7.589448 h -7.83496 v 7.589448 h -2.156213 z m -15.563168,0 h 13.481671 v 1.814636 H 18.740226 V 72.379166 H 16.573339 V 58.257029 H 10.915948 Z M 65.497127,39.809967 q -2.34835,0 -3.736011,1.750588 -1.376987,1.750588 -1.376987,4.771419 0,3.010157 1.376987,4.760745 1.387661,1.750588 3.736011,1.750588 2.348349,0 3.714662,-1.750588 1.376986,-1.750588 1.376986,-4.760745 0,-3.020831 -1.376986,-4.771419 -1.366313,-1.750588 -3.714662,-1.750588 z m 0,-1.750588 q 3.351735,0 5.358506,2.252281 2.006772,2.241606 2.006772,6.020314 0,3.768034 -2.006772,6.020315 -2.006771,2.241606 -5.358506,2.241606 -3.36241,0 -5.379856,-2.241606 -2.006771,-2.241607 -2.006771,-6.020315 0,-3.778708 2.006771,-6.020314 2.017446,-2.252281 5.379856,-2.252281 z m -22.821688,0.288206 h 2.903414 L 52.64525,51.679807 V 38.347585 h 2.092166 V 54.28434 H 51.834001 L 44.767605,40.952119 V 54.28434 h -2.092166 z" />
-</svg>
diff --git a/api/api.html b/api/api.html
index 49341efca..e0ae4234a 100644
--- a/api/api.html
+++ b/api/api.html
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/contributing.html b/api/contributing.html
index c36d343ab..bd73694d6 100644
--- a/api/contributing.html
+++ b/api/contributing.html
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/developing.html b/api/developing.html
index 0ece49be9..35cdea3b3 100644
--- a/api/developing.html
+++ b/api/developing.html
@@ -7,7 +7,7 @@
 
     
       <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Contributing to cyclops" href="contributing.html" /><link rel="prev" title="Chest X-Ray Disease Classification" href="tutorials/nihcxr/cxr_classification.html" />
+    <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Contributing to cyclops" href="contributing.html" /><link rel="prev" title="Example use cases" href="tutorials_use_cases.html" />
 
     <link rel="shortcut icon" href="_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
 
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
@@ -367,14 +359,14 @@ <h2>Contributing<a class="headerlink" href="#contributing" title="Link to this h
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
-          <a class="prev-page" href="tutorials/nihcxr/cxr_classification.html">
+          <a class="prev-page" href="tutorials_use_cases.html">
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
               <div class="page-info">
                 <div class="context">
                   <span>Previous</span>
                 </div>
                 
-                <div class="title">Chest X-Ray Disease Classification</div>
+                <div class="title">Example use cases</div>
                 
               </div>
             </a>
diff --git a/api/evaluation.html b/api/evaluation.html
index 71f916753..e5305a784 100644
--- a/api/evaluation.html
+++ b/api/evaluation.html
@@ -7,7 +7,7 @@
 
     
       <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Breast Cancer Classification and Evaluation" href="examples/metrics.html" /><link rel="prev" title="Installation" href="installation.html" />
+    <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Model Report" href="model_report.html" /><link rel="prev" title="Installation" href="installation.html" />
 
     <link rel="shortcut icon" href="_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
 
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 current has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 current has-children current-page"><a class="current reference internal" href="#">Evaluation</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 current has-children current-page"><a class="current reference internal" href="#">Evaluation</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
@@ -347,18 +339,6 @@ <h2>Key capabilities<a class="headerlink" href="#key-capabilities" title="Link t
 </div></blockquote>
 <p>Follow the example below for the instructions:</p>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="examples/metrics.html#Features">Features</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/metrics.html#Individual-Metrics">Individual Metrics</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/metrics.html#Using-MetricDict">Using <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code></a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/metrics.html#Data-Slicing">Data Slicing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/metrics.html#Intersectional-slicing">Intersectional slicing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/metrics.html#Preparing-Result">Preparing Result</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/metrics.html#Fairness-Evaluator">Fairness Evaluator</a></li>
-</ul>
-</li>
-</ul>
 </div>
 </section>
 </section>
@@ -368,12 +348,12 @@ <h2>Key capabilities<a class="headerlink" href="#key-capabilities" title="Link t
       <footer>
         
         <div class="related-pages">
-          <a class="next-page" href="examples/metrics.html">
+          <a class="next-page" href="model_report.html">
               <div class="page-info">
                 <div class="context">
                   <span>Next</span>
                 </div>
-                <div class="title">Breast Cancer Classification and Evaluation</div>
+                <div class="title">Model Report</div>
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
diff --git a/api/examples/metrics.html b/api/examples/metrics.html
deleted file mode 100644
index b882e551e..000000000
--- a/api/examples/metrics.html
+++ /dev/null
@@ -1,688 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en">
-  <head><meta charset="utf-8"/>
-    <meta name="viewport" content="width=device-width,initial-scale=1"/>
-    <meta name="color-scheme" content="light dark">
-    <meta name="google-site-verification" content="i0qQRaR9OA3tSz_9tDocdcXGY27Ox_cy4FrvTHD2C_0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-
-    
-      <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="Model Report" href="../model_report.html" /><link rel="prev" title="Evaluation" href="../evaluation.html" />
-
-    <link rel="shortcut icon" href="../_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
-
-    
-    <meta name="msvalidate.01" content="8D2A5032F006424CAF54C0DDCD16F666" />
-      
-        <title>Breast Cancer Classification and Evaluation - cyclops documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
-    <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
-    <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../_static/css/cyclops.css?v=9f20b50b" />
-    
-    
-
-
-<style>
-  body {
-    --color-code-background: #f8f8f8;
-  --color-code-foreground: black;
-  --color-card-background: #5e88f2;
-  
-  }
-  @media not print {
-    body[data-theme="dark"] {
-      --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-    }
-    @media (prefers-color-scheme: dark) {
-      body:not([data-theme="light"]) {
-        --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-      }
-    }
-  }
-</style><script src="../_static/documentation_options.js?v=5929fcd5"></script>
-    <script src="../_static/doctools.js?v=888ff710"></script>
-    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="../_static/scripts/furo.js?v=32e29ea5"></script>
-    <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
-    <script src="../_static/copybutton.js?v=ccdb6887"></script>
-    <script src="../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-    <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script src="../_static/require.min.js?v=53367eec"></script>
-    <script src="../_static/custom.js?v=275847af"></script>
-    
-    <script src="https://kit.fontawesome.com/2c1f516901.js" crossorigin="anonymous"></script>
-
-  </head>
-  <body>
-    
-    <script>
-      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
-    </script>
-    
-
-<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
-  <symbol id="svg-toc" viewBox="0 0 24 24">
-    <title>Contents</title>
-    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
-      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-menu" viewBox="0 0 24 24">
-    <title>Menu</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
-      <line x1="3" y1="12" x2="21" y2="12"></line>
-      <line x1="3" y1="6" x2="21" y2="6"></line>
-      <line x1="3" y1="18" x2="21" y2="18"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
-    <title>Expand</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
-      <polyline points="9 18 15 12 9 6"></polyline>
-    </svg>
-  </symbol>
-  <symbol id="svg-sun" viewBox="0 0 24 24">
-    <title>Light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
-      <circle cx="12" cy="12" r="5"></circle>
-      <line x1="12" y1="1" x2="12" y2="3"></line>
-      <line x1="12" y1="21" x2="12" y2="23"></line>
-      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
-      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
-      <line x1="1" y1="12" x2="3" y2="12"></line>
-      <line x1="21" y1="12" x2="23" y2="12"></line>
-      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
-      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon" viewBox="0 0 24 24">
-    <title>Dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
-      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
-    </svg>
-  </symbol>
-  <symbol id="svg-sun-half" viewBox="0 0 24 24">
-    <title>Auto light/dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-      <circle cx="12" cy="12" r="9" />
-      <path d="M13 12h5" />
-      <path d="M13 15h4" />
-      <path d="M13 18h1" />
-      <path d="M13 9h4" />
-      <path d="M13 6h1" />
-    </svg>
-  </symbol>
-</svg>
-
-<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
-<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
-<label class="overlay sidebar-overlay" for="__navigation">
-  <div class="visually-hidden">Hide navigation sidebar</div>
-</label>
-<label class="overlay toc-overlay" for="__toc">
-  <div class="visually-hidden">Hide table of contents sidebar</div>
-</label>
-
-
-
-<div class="page">
-  <header class="mobile-header">
-    <div class="header-left">
-      <label class="nav-overlay-icon" for="__navigation">
-        <div class="visually-hidden">Toggle site navigation sidebar</div>
-        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
-      </label>
-    </div>
-    <div class="header-center">
-      <a href="../index.html"><div class="brand">cyclops  documentation</div></a>
-    </div>
-    <div class="header-right">
-      <div class="theme-toggle-container theme-toggle-header">
-        <button class="theme-toggle">
-          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-        </button>
-      </div>
-      <label class="toc-overlay-icon toc-header-icon" for="__toc">
-        <div class="visually-hidden">Toggle table of contents sidebar</div>
-        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-      </label>
-    </div>
-  </header>
-  <aside class="sidebar-drawer">
-    <div class="sidebar-container">
-      
-      <div class="sidebar-sticky"><a class="sidebar-brand" href="../index.html">
-  
-  <div class="sidebar-logo-container">
-    <img class="sidebar-logo" src="../_static/cyclops_logo-dark.png" alt="Logo"/>
-  </div>
-  
-  
-</a><form class="sidebar-search-container" method="get" action="../search.html" role="search">
-  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
-  <input type="hidden" name="check_keywords" value="yes">
-  <input type="hidden" name="area" value="default">
-</form>
-<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <ul class="current">
-<li class="toctree-l1 current has-children"><a class="reference internal" href="../user_guide.html">User Guide</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l2 current has-children"><a class="reference internal" href="../evaluation.html">Evaluation</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">Breast Cancer Classification and Evaluation</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="report.html">Report Generation for Heart Failure Prediction</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../monitoring.html">Monitoring</a></li>
-</ul>
-</li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../developing.html">🧑🏿‍💻 Developing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../contributing.html">Contributing to cyclops</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../api.html">API Reference</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of API Reference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../reference/api/cyclops.data.html">cyclops.data</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of cyclops.data</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.html">cyclops.data.slicer</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of cyclops.data.slicer</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.compound_filter.html">cyclops.data.slicer.compound_filter</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html">cyclops.data.slicer.filter_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html">cyclops.data.slicer.filter_non_null</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.filter_range.html">cyclops.data.slicer.filter_range</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html">cyclops.data.slicer.filter_string_contains</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.filter_value.html">cyclops.data.slicer.filter_value</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.is_datetime.html">cyclops.data.slicer.is_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.overall.html">cyclops.data.slicer.overall</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html">cyclops.data.slicer.SliceSpec</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.aggregate.html">cyclops.data.aggregate</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of cyclops.data.aggregate</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html">cyclops.data.aggregate.tabular_as_aggregated</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html">cyclops.data.aggregate.timestamp_ffill_agg</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html">cyclops.data.aggregate.Aggregator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.features.medical_image.html">cyclops.data.features.medical_image</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of cyclops.data.features.medical_image</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html">cyclops.data.features.medical_image.MedicalImage</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../reference/api/cyclops.tasks.html">cyclops.tasks</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of cyclops.tasks</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.tasks.classification.html">cyclops.tasks.classification</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of cyclops.tasks.classification</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html">cyclops.tasks.classification.BinaryTabularClassificationTask</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html">cyclops.tasks.classification.MultilabelImageClassificationTask</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../reference/api/evaluator.html">Evaluator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../reference/api/fairness_evaluator.html">Fairness Evaluator</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../reference/api/metrics.html">Evaluation Metrics</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of Evaluation Metrics</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/accuracy.html">Accuracy</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/auroc.html">AUROC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/average_precision.html">Average Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/f1score.html">F1-Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/f_beta.html">F-beta Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/precision.html">Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/precision_recall_curve.html">PrecisionRecallCurve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/recall.html">Recall</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/roc.html">ROC Curve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/sensitivity.html">Sensitivity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/specificity.html">Specificity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/stat_scores.html">StatScores</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../reference/api/cyclops.monitor.html">cyclops.monitor</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of cyclops.monitor</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.clinical_applicator.html">cyclops.monitor.clinical_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of cyclops.monitor.clinical_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html">cyclops.monitor.clinical_applicator.ClinicalShiftApplicator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html">cyclops.monitor.synthetic_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of cyclops.monitor.synthetic_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html">cyclops.monitor.synthetic_applicator.binary_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html">cyclops.monitor.synthetic_applicator.feature_association_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html">cyclops.monitor.synthetic_applicator.feature_swap_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html">cyclops.monitor.synthetic_applicator.gaussian_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html">cyclops.monitor.synthetic_applicator.knockout_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html">cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../reference/api/cyclops.report.html">cyclops.report</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of cyclops.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.report.report.html">cyclops.report.report</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of cyclops.report.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.report.report.ModelCardReport.html">cyclops.report.report.ModelCardReport</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-</div>
-</div>
-
-      </div>
-      
-    </div>
-  </aside>
-  <div class="main">
-    <div class="content">
-      <div class="article-container">
-        <a href="#" class="back-to-top muted-link">
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
-            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
-          </svg>
-          <span>Back to top</span>
-        </a>
-        <div class="content-icon-container">
-          
-<div class="theme-toggle-container theme-toggle-content">
-            <button class="theme-toggle">
-              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-            </button>
-          </div>
-          <label class="toc-overlay-icon toc-content-icon" for="__toc">
-            <div class="visually-hidden">Toggle table of contents sidebar</div>
-            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-          </label>
-        </div>
-        <article role="main">
-          <section id="Breast-Cancer-Classification-and-Evaluation">
-<h1>Breast Cancer Classification and Evaluation<a class="headerlink" href="#Breast-Cancer-Classification-and-Evaluation" title="Link to this heading">#</a></h1>
-<p>The Breast Cancer dataset is a well-suited example for demonstrating CyclOps features due to its two distinct classes (binary classification) and complete absence of missing values. This clean and organized structure makes it an ideal starting point for exploring CyclOps Evaluator.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">datasets.arrow_dataset</span> <span class="kn">import</span> <span class="n">Dataset</span>
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
-<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
-
-<span class="kn">from</span> <span class="nn">cyclops.data.slicer</span> <span class="kn">import</span> <span class="n">SliceSpec</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate</span> <span class="kn">import</span> <span class="n">evaluator</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.fairness</span> <span class="kn">import</span> <span class="n">evaluate_fairness</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics</span> <span class="kn">import</span> <span class="n">BinaryAccuracy</span><span class="p">,</span> <span class="n">create_metric</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics.experimental</span> <span class="kn">import</span> <span class="n">BinaryAUROC</span><span class="p">,</span> <span class="n">BinaryAveragePrecision</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics.experimental.metric_dict</span> <span class="kn">import</span> <span class="n">MetricDict</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.plot.classification</span> <span class="kn">import</span> <span class="n">ClassificationPlotter</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Loading the data</span>
-<span class="n">breast_cancer_data</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_breast_cancer</span><span class="p">(</span><span class="n">as_frame</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">breast_cancer_data</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">breast_cancer_data</span><span class="o">.</span><span class="n">target</span>
-</pre></div>
-</div>
-</div>
-<section id="Features">
-<h2>Features<a class="headerlink" href="#Features" title="Link to this heading">#</a></h2>
-<p>Just taking a quick look at features and their stats…</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">breast_cancer_data</span><span class="o">.</span><span class="n">frame</span>
-<span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">T</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Splitting into train and test</span>
-<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
-    <span class="n">X</span><span class="p">,</span>
-    <span class="n">y</span><span class="p">,</span>
-    <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-    <span class="n">random_state</span><span class="o">=</span><span class="mi">13</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># Use SVM classifier for binary classification</span>
-<span class="n">svc</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">probability</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">svc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
-
-<span class="c1"># model predictions</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="n">svc</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="n">y_pred_prob</span> <span class="o">=</span> <span class="n">svc</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Now we can use CyclOps evaluation metrics to evaluate our model’s performance. You can either use each metric individually by calling them, or define a <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code> object. Here, we show both methods.</p>
-</section>
-<section id="Individual-Metrics">
-<h2>Individual Metrics<a class="headerlink" href="#Individual-Metrics" title="Link to this heading">#</a></h2>
-<p>In case you need only a single metric, you can create an object of the desired metric and call it on your ground truth and predictions:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bin_acc_metric</span> <span class="o">=</span> <span class="n">BinaryAccuracy</span><span class="p">()</span>
-<span class="n">bin_acc_metric</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">y_pred</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Using-MetricDict">
-<h2>Using <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code><a class="headerlink" href="#Using-MetricDict" title="Link to this heading">#</a></h2>
-<p>You may define a collection of metrics in case you need more metrics. It also speeds up the metric calculation.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metric_names</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;binary_accuracy&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_precision&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_recall&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_f1_score&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_roc_curve&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric_name</span> <span class="ow">in</span> <span class="n">metric_names</span>
-<span class="p">]</span>
-<span class="n">metric_collection</span> <span class="o">=</span> <span class="n">MetricDict</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
-<span class="n">metric_collection</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">y_pred</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<p>You may reset the metrics collection and add other metrics:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metric_collection</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
-<span class="n">metric_collection</span><span class="o">.</span><span class="n">add_metrics</span><span class="p">(</span><span class="n">BinaryAveragePrecision</span><span class="p">(),</span> <span class="n">BinaryAUROC</span><span class="p">())</span>
-<span class="n">metric_collection</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">y_pred</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Data-Slicing">
-<h2>Data Slicing<a class="headerlink" href="#Data-Slicing" title="Link to this heading">#</a></h2>
-<p>In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulation or subsets. We can define these subsets using <code class="docutils literal notranslate"><span class="pre">SliceSpec</span></code> objects.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">spec_list</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;worst radius&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mf">14.0</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mf">15.0</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;worst radius&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mf">15.0</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mf">17.0</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;worst texture&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mf">23.1</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mf">28.7</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-<span class="p">]</span>
-<span class="n">slice_spec</span> <span class="o">=</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Intersectional-slicing">
-<h2>Intersectional slicing<a class="headerlink" href="#Intersectional-slicing" title="Link to this heading">#</a></h2>
-<p>When subpopulation slices are specified using the <code class="docutils literal notranslate"><span class="pre">SliceSpec</span></code>, sometimes we wish create combinations of intersectional slices. We can use the <code class="docutils literal notranslate"><span class="pre">intersections</span></code> argument to specify this.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">slice_spec</span> <span class="o">=</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="p">,</span> <span class="n">intersections</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">slice_spec</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Preparing-Result">
-<h2>Preparing Result<a class="headerlink" href="#Preparing-Result" title="Link to this heading">#</a></h2>
-<p>CyclOps Evaluator takes data as a HuggingFace Dataset object, so we combine predictions and features in a dataframe, and create a <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> object:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Combine result and features for test data</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">X_test</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;target&quot;</span><span class="p">])],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">df</span><span class="p">[</span><span class="s2">&quot;preds&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_pred</span>
-<span class="n">df</span><span class="p">[</span><span class="s2">&quot;preds_prob&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_pred_prob</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create Dataset object</span>
-<span class="n">breast_cancer_data</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-<span class="n">breast_cancer_sliced_result</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="n">breast_cancer_data</span><span class="p">,</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="n">metric_collection</span><span class="p">,</span>  <span class="c1"># type: ignore[list-item]</span>
-    <span class="n">target_columns</span><span class="o">=</span><span class="s2">&quot;target&quot;</span><span class="p">,</span>
-    <span class="n">prediction_columns</span><span class="o">=</span><span class="s2">&quot;preds_prob&quot;</span><span class="p">,</span>
-    <span class="n">slice_spec</span><span class="o">=</span><span class="n">slice_spec</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>We can visualize the <code class="docutils literal notranslate"><span class="pre">BinaryF1Score</span></code> and <code class="docutils literal notranslate"><span class="pre">BinaryPrecision</span></code> for the different slices</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the metric values for all the slices.</span>
-<span class="n">slice_metrics</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="p">{</span>
-        <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-        <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">slice_results</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">metric_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryF1Score&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecision&quot;</span><span class="p">]</span>
-    <span class="p">}</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">breast_cancer_sliced_result</span><span class="p">[</span>
-        <span class="s2">&quot;model_for_preds_prob&quot;</span>
-    <span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="c1"># Plotting the metric values for all the slices.</span>
-<span class="n">plotter</span> <span class="o">=</span> <span class="n">ClassificationPlotter</span><span class="p">(</span><span class="n">task_type</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">])</span>
-<span class="n">plotter</span><span class="o">.</span><span class="n">set_template</span><span class="p">(</span><span class="s2">&quot;plotly_white&quot;</span><span class="p">)</span>
-<span class="n">slice_metrics_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_comparison_bar</span><span class="p">(</span><span class="n">slice_metrics</span><span class="p">)</span>
-<span class="n">slice_metrics_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Fairness-Evaluator">
-<h2>Fairness Evaluator<a class="headerlink" href="#Fairness-Evaluator" title="Link to this heading">#</a></h2>
-<p>The Breast Cancer dataset may not be a very good example to apply fairness, but to demonstrate how you can use our fairness evaluator, we apply it to <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">texture</span></code> feature. It’s recommended to use it on features with discrete values. For optimal results, the feature should have less than 50 unique categories.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fairness_result</span> <span class="o">=</span> <span class="n">evaluate_fairness</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="n">breast_cancer_data</span><span class="p">,</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="s2">&quot;binary_precision&quot;</span><span class="p">,</span>  <span class="c1"># type: ignore[list-item]</span>
-    <span class="n">groups</span><span class="o">=</span><span class="s2">&quot;mean texture&quot;</span><span class="p">,</span>
-    <span class="n">target_columns</span><span class="o">=</span><span class="s2">&quot;target&quot;</span><span class="p">,</span>
-    <span class="n">prediction_columns</span><span class="o">=</span><span class="s2">&quot;preds_prob&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fairness_result</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-
-        </article>
-      </div>
-      <footer>
-        
-        <div class="related-pages">
-          <a class="next-page" href="../model_report.html">
-              <div class="page-info">
-                <div class="context">
-                  <span>Next</span>
-                </div>
-                <div class="title">Model Report</div>
-              </div>
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-            </a>
-          <a class="prev-page" href="../evaluation.html">
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-              <div class="page-info">
-                <div class="context">
-                  <span>Previous</span>
-                </div>
-                
-                <div class="title">Evaluation</div>
-                
-              </div>
-            </a>
-        </div>
-        <div class="bottom-of-page">
-          <div class="left-details">
-            <div class="copyright">
-                Copyright &#169; 2024, Vector AI Engineering
-            </div>
-            <!--
-            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-            
-            <a href="https://github.com/pradyunsg/furo">Furo</a>
-            
-            -->
-          </div>
-          <div class="right-details">
-            <div class="icons">
-              <a class="muted-link " href="https://github.com/VectorInstitute/cyclops" aria-label="GitHub">
-                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
-                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
-                </svg>
-            </a>
-              
-            </div>
-          </div>
-        </div>
-        <div class="footer-container" style="text-align: center;">
-          <div class="footer-small-text">
-              <br> All <a href="https://github.com/VectorInstitute/cyclops">source code for cyclops</a> is freely available under the terms of an <a href="https://opensource.org/licenses/Apache-2.0">Apache-2.0</a> license.
-          </div>
-
-          <div class="footer-small-text">
-            This work is made possible due to the data obtained from the <a href="https://www.geminimedicine.ca/">GEMINI study</a>.
-          </div>
-
-          <div class="logo">
-            <a class="logo" href="https://vectorinstitute.ai/">
-              <span class="logo"></span>
-              <img alt="Logo of the Vector Institute" style="width:90px; max-width:90px;" src="../_static/logos/vector_logo.png" />
-            </a>
-            <a class="logo" href="https://www.geminimedicine.ca/">
-              <span class="logo"></span>
-              <img alt="Logo of GEMINI" style="width:90px; max-width:90px;" src="../_static/logos/gemini_logo.png" />
-            </a>
-          </div>
-        </div>
-        
-      </footer>
-    </div>
-    <aside class="toc-drawer">
-      
-      
-      <div class="toc-sticky toc-scroll">
-        <div class="toc-title-container">
-          <span class="toc-title">
-            On this page
-          </span>
-        </div>
-        <div class="toc-tree-container">
-          <div class="toc-tree">
-            <ul>
-<li><a class="reference internal" href="#">Breast Cancer Classification and Evaluation</a><ul>
-<li><a class="reference internal" href="#Features">Features</a></li>
-<li><a class="reference internal" href="#Individual-Metrics">Individual Metrics</a></li>
-<li><a class="reference internal" href="#Using-MetricDict">Using <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code></a></li>
-<li><a class="reference internal" href="#Data-Slicing">Data Slicing</a></li>
-<li><a class="reference internal" href="#Intersectional-slicing">Intersectional slicing</a></li>
-<li><a class="reference internal" href="#Preparing-Result">Preparing Result</a></li>
-<li><a class="reference internal" href="#Fairness-Evaluator">Fairness Evaluator</a></li>
-</ul>
-</li>
-</ul>
-
-          </div>
-        </div>
-      </div>
-      
-      
-    </aside>
-  </div>
-</div></body>
-</html>
\ No newline at end of file
diff --git a/api/examples/metrics.ipynb b/api/examples/metrics.ipynb
deleted file mode 100644
index cf2b10ba4..000000000
--- a/api/examples/metrics.ipynb
+++ /dev/null
@@ -1,351 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Breast Cancer Classification and Evaluation\n",
-    "\n",
-    "The Breast Cancer dataset is a well-suited example for demonstrating CyclOps features due to its two distinct classes (binary classification) and complete absence of missing values. This clean and organized structure makes it an ideal starting point for exploring CyclOps Evaluator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from datasets.arrow_dataset import Dataset\n",
-    "from sklearn import datasets\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate import evaluator\n",
-    "from cyclops.evaluate.fairness import evaluate_fairness\n",
-    "from cyclops.evaluate.metrics import BinaryAccuracy, create_metric\n",
-    "from cyclops.evaluate.metrics.experimental import BinaryAUROC, BinaryAveragePrecision\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Loading the data\n",
-    "breast_cancer_data = datasets.load_breast_cancer(as_frame=True)\n",
-    "X, y = breast_cancer_data.data, breast_cancer_data.target"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Features\n",
-    "Just taking a quick look at features and their stats..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = breast_cancer_data.frame\n",
-    "df.describe().T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Splitting into train and test\n",
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    X,\n",
-    "    y,\n",
-    "    test_size=0.1,\n",
-    "    random_state=13,\n",
-    ")\n",
-    "\n",
-    "# Use SVM classifier for binary classification\n",
-    "svc = SVC(C=10, gamma=0.01, probability=True)\n",
-    "svc.fit(X_train, y_train)\n",
-    "\n",
-    "# model predictions\n",
-    "y_pred = svc.predict(X_test)\n",
-    "y_pred_prob = svc.predict_proba(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use CyclOps evaluation metrics to evaluate our model's performance. You can either use each metric individually by calling them, or define a ``MetricDict`` object.\n",
-    "Here, we show both methods."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Individual Metrics\n",
-    "In case you need only a single metric, you can create an object of the desired metric and call it on your ground truth and predictions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bin_acc_metric = BinaryAccuracy()\n",
-    "bin_acc_metric(y_test.values, np.float64(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using ``MetricDict``\n",
-    "You may define a collection of metrics in case you need more metrics. It also speeds up the metric calculation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_roc_curve\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)\n",
-    "metric_collection(y_test.values, np.float64(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You may reset the metrics collection and add other metrics:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_collection.reset()\n",
-    "metric_collection.add_metrics(BinaryAveragePrecision(), BinaryAUROC())\n",
-    "metric_collection(y_test.values, np.float64(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data Slicing\n",
-    "\n",
-    "In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulation or subsets. We can define these subsets using ``SliceSpec`` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"worst radius\": {\n",
-    "            \"min_value\": 14.0,\n",
-    "            \"max_value\": 15.0,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"worst radius\": {\n",
-    "            \"min_value\": 15.0,\n",
-    "            \"max_value\": 17.0,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"worst texture\": {\n",
-    "            \"min_value\": 23.1,\n",
-    "            \"max_value\": 28.7,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Intersectional slicing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When subpopulation slices are specified using the ``SliceSpec``, sometimes we wish create combinations of intersectional slices. We can use the ``intersections`` argument to specify this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "slice_spec = SliceSpec(spec_list, intersections=2)\n",
-    "slice_spec"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Preparing Result\n",
-    "\n",
-    "CyclOps Evaluator takes data as a HuggingFace Dataset object, so we combine predictions and features in a dataframe, and create a `Dataset` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Combine result and features for test data\n",
-    "df = pd.concat([X_test, pd.DataFrame(y_test, columns=[\"target\"])], axis=1)\n",
-    "df[\"preds\"] = y_pred\n",
-    "df[\"preds_prob\"] = y_pred_prob[:, 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Dataset object\n",
-    "breast_cancer_data = Dataset.from_pandas(df)\n",
-    "breast_cancer_sliced_result = evaluator.evaluate(\n",
-    "    dataset=breast_cancer_data,\n",
-    "    metrics=metric_collection,  # type: ignore[list-item]\n",
-    "    target_columns=\"target\",\n",
-    "    prediction_columns=\"preds_prob\",\n",
-    "    slice_spec=slice_spec,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can visualize the ``BinaryF1Score`` and ``BinaryPrecision`` for the different slices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name in [\"BinaryF1Score\", \"BinaryPrecision\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in breast_cancer_sliced_result[\n",
-    "        \"model_for_preds_prob\"\n",
-    "    ].items()\n",
-    "}\n",
-    "# Plotting the metric values for all the slices.\n",
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Fairness Evaluator\n",
-    "\n",
-    "The Breast Cancer dataset may not be a very good example to apply fairness, but to demonstrate how you can use our fairness evaluator, we apply it to `mean texture` feature. It's recommended to use it on features with discrete values. For optimal results, the feature should have less than 50 unique categories."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fairness_result = evaluate_fairness(\n",
-    "    dataset=breast_cancer_data,\n",
-    "    metrics=\"binary_precision\",  # type: ignore[list-item]\n",
-    "    groups=\"mean texture\",\n",
-    "    target_columns=\"target\",\n",
-    "    prediction_columns=\"preds_prob\",\n",
-    ")\n",
-    "fairness_result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/api/examples/report.html b/api/examples/report.html
deleted file mode 100644
index d740eaf74..000000000
--- a/api/examples/report.html
+++ /dev/null
@@ -1,1179 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en">
-  <head><meta charset="utf-8"/>
-    <meta name="viewport" content="width=device-width,initial-scale=1"/>
-    <meta name="color-scheme" content="light dark">
-    <meta name="google-site-verification" content="i0qQRaR9OA3tSz_9tDocdcXGY27Ox_cy4FrvTHD2C_0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-
-    
-      <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="Monitoring" href="../monitoring.html" /><link rel="prev" title="Model Report" href="../model_report.html" />
-
-    <link rel="shortcut icon" href="../_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
-
-    
-    <meta name="msvalidate.01" content="8D2A5032F006424CAF54C0DDCD16F666" />
-      
-        <title>Report Generation for Heart Failure Prediction - cyclops documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
-    <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
-    <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../_static/css/cyclops.css?v=9f20b50b" />
-    
-    
-
-
-<style>
-  body {
-    --color-code-background: #f8f8f8;
-  --color-code-foreground: black;
-  --color-card-background: #5e88f2;
-  
-  }
-  @media not print {
-    body[data-theme="dark"] {
-      --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-    }
-    @media (prefers-color-scheme: dark) {
-      body:not([data-theme="light"]) {
-        --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-      }
-    }
-  }
-</style><script src="../_static/documentation_options.js?v=5929fcd5"></script>
-    <script src="../_static/doctools.js?v=888ff710"></script>
-    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="../_static/scripts/furo.js?v=32e29ea5"></script>
-    <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
-    <script src="../_static/copybutton.js?v=ccdb6887"></script>
-    <script src="../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-    <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script src="../_static/require.min.js?v=53367eec"></script>
-    <script src="../_static/custom.js?v=275847af"></script>
-    
-    <script src="https://kit.fontawesome.com/2c1f516901.js" crossorigin="anonymous"></script>
-
-  </head>
-  <body>
-    
-    <script>
-      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
-    </script>
-    
-
-<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
-  <symbol id="svg-toc" viewBox="0 0 24 24">
-    <title>Contents</title>
-    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
-      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-menu" viewBox="0 0 24 24">
-    <title>Menu</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
-      <line x1="3" y1="12" x2="21" y2="12"></line>
-      <line x1="3" y1="6" x2="21" y2="6"></line>
-      <line x1="3" y1="18" x2="21" y2="18"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
-    <title>Expand</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
-      <polyline points="9 18 15 12 9 6"></polyline>
-    </svg>
-  </symbol>
-  <symbol id="svg-sun" viewBox="0 0 24 24">
-    <title>Light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
-      <circle cx="12" cy="12" r="5"></circle>
-      <line x1="12" y1="1" x2="12" y2="3"></line>
-      <line x1="12" y1="21" x2="12" y2="23"></line>
-      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
-      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
-      <line x1="1" y1="12" x2="3" y2="12"></line>
-      <line x1="21" y1="12" x2="23" y2="12"></line>
-      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
-      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon" viewBox="0 0 24 24">
-    <title>Dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
-      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
-    </svg>
-  </symbol>
-  <symbol id="svg-sun-half" viewBox="0 0 24 24">
-    <title>Auto light/dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-      <circle cx="12" cy="12" r="9" />
-      <path d="M13 12h5" />
-      <path d="M13 15h4" />
-      <path d="M13 18h1" />
-      <path d="M13 9h4" />
-      <path d="M13 6h1" />
-    </svg>
-  </symbol>
-</svg>
-
-<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
-<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
-<label class="overlay sidebar-overlay" for="__navigation">
-  <div class="visually-hidden">Hide navigation sidebar</div>
-</label>
-<label class="overlay toc-overlay" for="__toc">
-  <div class="visually-hidden">Hide table of contents sidebar</div>
-</label>
-
-
-
-<div class="page">
-  <header class="mobile-header">
-    <div class="header-left">
-      <label class="nav-overlay-icon" for="__navigation">
-        <div class="visually-hidden">Toggle site navigation sidebar</div>
-        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
-      </label>
-    </div>
-    <div class="header-center">
-      <a href="../index.html"><div class="brand">cyclops  documentation</div></a>
-    </div>
-    <div class="header-right">
-      <div class="theme-toggle-container theme-toggle-header">
-        <button class="theme-toggle">
-          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-        </button>
-      </div>
-      <label class="toc-overlay-icon toc-header-icon" for="__toc">
-        <div class="visually-hidden">Toggle table of contents sidebar</div>
-        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-      </label>
-    </div>
-  </header>
-  <aside class="sidebar-drawer">
-    <div class="sidebar-container">
-      
-      <div class="sidebar-sticky"><a class="sidebar-brand" href="../index.html">
-  
-  <div class="sidebar-logo-container">
-    <img class="sidebar-logo" src="../_static/cyclops_logo-dark.png" alt="Logo"/>
-  </div>
-  
-  
-</a><form class="sidebar-search-container" method="get" action="../search.html" role="search">
-  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
-  <input type="hidden" name="check_keywords" value="yes">
-  <input type="hidden" name="area" value="default">
-</form>
-<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <ul class="current">
-<li class="toctree-l1 current has-children"><a class="reference internal" href="../user_guide.html">User Guide</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="metrics.html">Breast Cancer Classification and Evaluation</a></li>
-</ul>
-</li>
-<li class="toctree-l2 current has-children"><a class="reference internal" href="../model_report.html">Model Report</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">Report Generation for Heart Failure Prediction</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../monitoring.html">Monitoring</a></li>
-</ul>
-</li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../developing.html">🧑🏿‍💻 Developing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../contributing.html">Contributing to cyclops</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../api.html">API Reference</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of API Reference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../reference/api/cyclops.data.html">cyclops.data</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of cyclops.data</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.html">cyclops.data.slicer</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of cyclops.data.slicer</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.compound_filter.html">cyclops.data.slicer.compound_filter</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html">cyclops.data.slicer.filter_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html">cyclops.data.slicer.filter_non_null</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.filter_range.html">cyclops.data.slicer.filter_range</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html">cyclops.data.slicer.filter_string_contains</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.filter_value.html">cyclops.data.slicer.filter_value</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.is_datetime.html">cyclops.data.slicer.is_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.overall.html">cyclops.data.slicer.overall</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html">cyclops.data.slicer.SliceSpec</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.aggregate.html">cyclops.data.aggregate</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of cyclops.data.aggregate</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html">cyclops.data.aggregate.tabular_as_aggregated</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html">cyclops.data.aggregate.timestamp_ffill_agg</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html">cyclops.data.aggregate.Aggregator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.features.medical_image.html">cyclops.data.features.medical_image</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of cyclops.data.features.medical_image</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html">cyclops.data.features.medical_image.MedicalImage</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../reference/api/cyclops.tasks.html">cyclops.tasks</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of cyclops.tasks</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.tasks.classification.html">cyclops.tasks.classification</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of cyclops.tasks.classification</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html">cyclops.tasks.classification.BinaryTabularClassificationTask</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html">cyclops.tasks.classification.MultilabelImageClassificationTask</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../reference/api/evaluator.html">Evaluator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../reference/api/fairness_evaluator.html">Fairness Evaluator</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../reference/api/metrics.html">Evaluation Metrics</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of Evaluation Metrics</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/accuracy.html">Accuracy</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/auroc.html">AUROC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/average_precision.html">Average Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/f1score.html">F1-Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/f_beta.html">F-beta Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/precision.html">Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/precision_recall_curve.html">PrecisionRecallCurve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/recall.html">Recall</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/roc.html">ROC Curve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/sensitivity.html">Sensitivity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/specificity.html">Specificity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../reference/api/metrics/stat_scores.html">StatScores</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../reference/api/cyclops.monitor.html">cyclops.monitor</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of cyclops.monitor</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.clinical_applicator.html">cyclops.monitor.clinical_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of cyclops.monitor.clinical_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html">cyclops.monitor.clinical_applicator.ClinicalShiftApplicator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html">cyclops.monitor.synthetic_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of cyclops.monitor.synthetic_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html">cyclops.monitor.synthetic_applicator.binary_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html">cyclops.monitor.synthetic_applicator.feature_association_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html">cyclops.monitor.synthetic_applicator.feature_swap_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html">cyclops.monitor.synthetic_applicator.gaussian_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html">cyclops.monitor.synthetic_applicator.knockout_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html">cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../reference/api/cyclops.report.html">cyclops.report</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of cyclops.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../reference/api/_autosummary/cyclops.report.report.html">cyclops.report.report</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of cyclops.report.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../reference/api/_autosummary/cyclops.report.report.ModelCardReport.html">cyclops.report.report.ModelCardReport</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-</div>
-</div>
-
-      </div>
-      
-    </div>
-  </aside>
-  <div class="main">
-    <div class="content">
-      <div class="article-container">
-        <a href="#" class="back-to-top muted-link">
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
-            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
-          </svg>
-          <span>Back to top</span>
-        </a>
-        <div class="content-icon-container">
-          
-<div class="theme-toggle-container theme-toggle-content">
-            <button class="theme-toggle">
-              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-            </button>
-          </div>
-          <label class="toc-overlay-icon toc-content-icon" for="__toc">
-            <div class="visually-hidden">Toggle table of contents sidebar</div>
-            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-          </label>
-        </div>
-        <article role="main">
-          <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">inspect</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">shutil</span>
-<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">date</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
-<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">Dataset</span>
-<span class="kn">from</span> <span class="nn">kaggle.api.kaggle_api_extended</span> <span class="kn">import</span> <span class="n">KaggleApi</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
-<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
-
-<span class="kn">from</span> <span class="nn">cyclops.data.slicer</span> <span class="kn">import</span> <span class="n">SliceSpec</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics</span> <span class="kn">import</span> <span class="n">create_metric</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics.experimental.metric_dict</span> <span class="kn">import</span> <span class="n">MetricDict</span>
-<span class="kn">from</span> <span class="nn">cyclops.report</span> <span class="kn">import</span> <span class="n">ModelCardReport</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.plot.classification</span> <span class="kn">import</span> <span class="n">ClassificationPlotter</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.utils</span> <span class="kn">import</span> <span class="n">flatten_results_dict</span>
-</pre></div>
-</div>
-</div>
-<section id="Report-Generation-for-Heart-Failure-Prediction">
-<h1>Report Generation for Heart Failure Prediction<a class="headerlink" href="#Report-Generation-for-Heart-Failure-Prediction" title="Link to this heading">#</a></h1>
-<p>Here’s an example to demonstrate how we can generate a report as we proceed through all the steps to train and evaluate a model. For this purpose, we are going to use Kaggle’s heart prediction failure dataset and gradually populate the report with information about dataset, model and results.</p>
-<section id="Create-Model-Card-Report">
-<h2>Create Model Card Report<a class="headerlink" href="#Create-Model-Card-Report" title="Link to this heading">#</a></h2>
-<p>First, we should create a <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> object to fill in the fields and sections after training.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">ModelCardReport</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Constants</span>
-<span class="n">DATA_DIR</span> <span class="o">=</span> <span class="s2">&quot;./data&quot;</span>
-<span class="n">RANDOM_SEED</span> <span class="o">=</span> <span class="mi">21</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Data-Loading">
-<h2>Data Loading<a class="headerlink" href="#Data-Loading" title="Link to this heading">#</a></h2>
-<p>Before starting, make sure to install the Kaggle API by running pip install kaggle. To use the Kaggle API, you need to sign up for a Kaggle account at <a class="reference external" href="https://www.kaggle.com">https://www.kaggle.com</a>. Then go to the ‘Account’ tab of your user profile (<a class="reference external" href="https://www.kaggle.com//account">https://www.kaggle.com//account</a>) and select ‘Create API Token’. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location ~/.kaggle/kaggle.json on your machine.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">api</span> <span class="o">=</span> <span class="n">KaggleApi</span><span class="p">()</span>
-<span class="n">api</span><span class="o">.</span><span class="n">authenticate</span><span class="p">()</span>
-<span class="n">api</span><span class="o">.</span><span class="n">dataset_download_files</span><span class="p">(</span>
-    <span class="s2">&quot;fedesoriano/heart-failure-prediction&quot;</span><span class="p">,</span>
-    <span class="n">path</span><span class="o">=</span><span class="n">DATA_DIR</span><span class="p">,</span>
-    <span class="n">unzip</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">DATA_DIR</span><span class="p">,</span> <span class="s2">&quot;heart.csv&quot;</span><span class="p">))</span>
-<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">T</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="s2">&quot;Sex&quot;</span><span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Sex Distribution&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Adding-figures-to-report">
-<h2>Adding figures to report<a class="headerlink" href="#Adding-figures-to-report" title="Link to this heading">#</a></h2>
-<p>We can add figures and diagrams to report. We can define caption and the section of the report that this figure belongs to. Since we are exploring the distribution of different features in the dataset, we add it to <code class="docutils literal notranslate"><span class="pre">datasets</span></code> section:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Sex Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<section id="Age-distribution-figure">
-<h3>Age distribution figure<a class="headerlink" href="#Age-distribution-figure" title="Link to this heading">#</a></h3>
-<p>We plot a histogram of ages similarly and add the figure to our report. This will appear under <strong>Dataset</strong> section:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">,</span>
-    <span class="n">bargap</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Outcome-distribution">
-<h3>Outcome distribution<a class="headerlink" href="#Outcome-distribution" title="Link to this heading">#</a></h3>
-<p>Plot outcome distribution and add it to report. Similarly, we also place it under <strong>Dataset</strong> section:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;HeartDisease&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;int&quot;</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;HeartDisease&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span><span class="n">textinfo</span><span class="o">=</span><span class="s2">&quot;percent+label&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span><span class="n">title_text</span><span class="o">=</span><span class="s2">&quot;Outcome Distribution&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span>
-    <span class="n">hovertemplate</span><span class="o">=</span><span class="s2">&quot;Outcome: %</span><span class="si">{label}</span><span class="s2">&lt;br&gt;Count: </span><span class="se">\</span>
-<span class="s2">    %</span><span class="si">{value}</span><span class="s2">&lt;br&gt;Percent: %</span><span class="si">{percent}</span><span class="s2">&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Outcome Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">class_counts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
-<span class="n">class_ratio</span> <span class="o">=</span> <span class="n">class_counts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">class_counts</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">class_ratio</span><span class="p">,</span> <span class="n">class_counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="Data-Preprocessing">
-<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Link to this heading">#</a></h2>
-<p>This dataset does not have any null values, so we can jump to feature scaling. The string data in the dataframe is in the form of object, we need to convert it back to string to work on it:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">string_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="s2">&quot;object&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span>
-<span class="n">df</span><span class="p">[</span><span class="n">string_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">string_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;string&quot;</span><span class="p">)</span>
-<span class="n">string_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="s2">&quot;string&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p>We are going to apply tree-based models to our data, so we use <code class="docutils literal notranslate"><span class="pre">LabelEncoder</span></code>:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">target</span> <span class="o">=</span> <span class="s2">&quot;outcome&quot;</span>
-
-<span class="n">df_processed</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">string_col</span><span class="p">,</span> <span class="n">drop_first</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="n">df_processed</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">feature_cols</span> <span class="o">=</span> <span class="n">df_processed</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
-<span class="n">feature_cols</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Before training, let’s document dataset in the model card. This can be done using the log_dataset method, which takes the following arguments:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">description</span></code>: A description of the dataset.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">citation</span></code>: The citation for the dataset.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">link</span></code>: A link to a resource for the dataset.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">license_id</span></code>: The SPDX license identifier for the dataset.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">version</span></code>: The version of the dataset.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">features</span></code>: A list of features in the dataset.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">split</span></code>: The split of the dataset (train, test, validation, etc.).</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">sensitive_features</span></code>: A list of sensitive features used to train/evaluate the model.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">sensitive_feature_justification</span></code>: A justification for the sensitive features used to train/evaluate the model.</p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_dataset</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;This dataset was created by combining different datasets</span>
-<span class="s2">    already available independently but not combined before. In this dataset,</span>
-<span class="s2">    5 heart datasets are combined over 11 common features. Every dataset used</span>
-<span class="s2">    can be found under the Index of heart disease datasets from UCI</span>
-<span class="s2">    Machine Learning Repository on the following link:</span>
-<span class="s2">    https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/.&quot;&quot;&quot;</span><span class="p">,</span>
-    <span class="n">citation</span><span class="o">=</span><span class="n">inspect</span><span class="o">.</span><span class="n">cleandoc</span><span class="p">(</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        @misc{fedesoriano,</span>
-<span class="sd">          title={Heart Failure Prediction Dataset.},</span>
-<span class="sd">          author={Fedesoriano, F},</span>
-<span class="sd">          year={2021},</span>
-<span class="sd">          publisher={Kaggle}</span>
-<span class="sd">        }</span>
-<span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
-    <span class="p">),</span>
-    <span class="n">link</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">    https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction</span>
-<span class="s2">    &quot;&quot;&quot;</span><span class="p">,</span>
-    <span class="n">license_id</span><span class="o">=</span><span class="s2">&quot;CC0-1.0&quot;</span><span class="p">,</span>
-    <span class="n">version</span><span class="o">=</span><span class="s2">&quot;Version 1&quot;</span><span class="p">,</span>
-    <span class="n">features</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">target</span><span class="p">),</span>
-    <span class="n">sensitive_features</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Sex&quot;</span><span class="p">,</span> <span class="s2">&quot;Age&quot;</span><span class="p">],</span>
-    <span class="n">sensitive_feature_justification</span><span class="o">=</span><span class="s2">&quot;Demographic information like age and gender </span><span class="se">\</span>
-<span class="s2">        often have a strong correlation with health outcomes. For example, older </span><span class="se">\</span>
-<span class="s2">        patients are more likely to have a higher risk of heart disease.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Create-HuggingFace-Dataset">
-<h2>Create HuggingFace Dataset<a class="headerlink" href="#Create-HuggingFace-Dataset" title="Link to this heading">#</a></h2>
-<p>We convert our processed Pandas dataframe into a Hugging Face dataset, for later evaluation by Cyclop metrics.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df_processed</span><span class="p">)</span>
-<span class="n">dataset</span><span class="o">.</span><span class="n">cleanup_cache_files</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Split dataframe into inputs and outputs</span>
-<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">df_processed</span><span class="p">[</span><span class="n">feature_cols</span><span class="p">],</span> <span class="n">df_processed</span><span class="p">[</span><span class="n">target</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Training">
-<h2>Training<a class="headerlink" href="#Training" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Splitting into train and test</span>
-<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
-    <span class="n">X</span><span class="p">,</span>
-    <span class="n">y</span><span class="p">,</span>
-    <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_SEED</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
-<span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
-<span class="n">X_train</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
-<span class="n">X_test</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="n">y_pred_prob</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Evaluation">
-<h2>Evaluation<a class="headerlink" href="#Evaluation" title="Link to this heading">#</a></h2>
-<p>As demonstrated in evaluation tutorial, we define a metric dict:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metric_names</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;binary_accuracy&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_precision&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_recall&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_f1_score&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_roc_curve&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_auroc&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric_name</span> <span class="ow">in</span> <span class="n">metric_names</span>
-<span class="p">]</span>
-<span class="n">metric_collection</span> <span class="o">=</span> <span class="n">MetricDict</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
-<span class="n">metric_collection</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">y_pred</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Data-Slicing">
-<h2>Data Slicing<a class="headerlink" href="#Data-Slicing" title="Link to this heading">#</a></h2>
-<p>In addition to overall metrics, it might be useful to see how the model performs on certain subpopulation or subsets. We can define these subsets using SliceSpec objects.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">spec_list</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Age&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">30</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Age&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">70</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-<span class="p">]</span>
-<span class="n">slice_spec</span> <span class="o">=</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Below, we are combining the raw features of the test data and the predictions so that we use them later for slice-specific evaluations.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get positions of matching indices in df</span>
-<span class="n">matching_positions</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_indexer</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
-
-<span class="c1"># Select rows from df using matching positions (valid positions are non-negative)</span>
-<span class="n">df_test</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">matching_positions</span><span class="p">[</span><span class="n">matching_positions</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">]]</span>
-<span class="n">df_test</span><span class="p">[</span><span class="s2">&quot;preds&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_pred</span>
-<span class="n">df_test</span><span class="p">[</span><span class="s2">&quot;preds_prob&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_pred_prob</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="n">df_test</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<section id="Age-distribution-in-test-data">
-<h3>Age distribution in test data<a class="headerlink" href="#Age-distribution-in-test-data" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">df_test</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Age Distribution in Test Data&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">,</span>
-    <span class="n">bargap</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="Logging-metrics-and-results-to-report">
-<h2>Logging metrics and results to report<a class="headerlink" href="#Logging-metrics-and-results-to-report" title="Link to this heading">#</a></h2>
-<p>Here, we gather evalutaions and add them to the report.</p>
-<p>We can add a performance metric to the model card using the <code class="docutils literal notranslate"><span class="pre">log_performance_metric</span></code> method, which expects a dictionary where the keys are in the following format: <code class="docutils literal notranslate"><span class="pre">slice_name/metric_name</span></code>. For instance, <code class="docutils literal notranslate"><span class="pre">overall/accuracy</span></code> or <code class="docutils literal notranslate"><span class="pre">Age:[30</span> <span class="pre">-</span> <span class="pre">50)/BinaryPrecision</span></code>.</p>
-<p>We first need to process the evaluation results to get the metrics in the right format.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cyclops.evaluate</span> <span class="kn">import</span> <span class="n">evaluator</span>
-
-
-<span class="c1"># Create Dataset object</span>
-<span class="n">heart_failure_data</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df_test</span><span class="p">)</span>
-
-<span class="n">result</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="n">heart_failure_data</span><span class="p">,</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="n">metric_collection</span><span class="p">,</span>  <span class="c1"># type: ignore[list-item]</span>
-    <span class="n">target_columns</span><span class="o">=</span><span class="n">target</span><span class="p">,</span>
-    <span class="n">prediction_columns</span><span class="o">=</span><span class="s2">&quot;preds_prob&quot;</span><span class="p">,</span>
-    <span class="n">slice_spec</span><span class="o">=</span><span class="n">slice_spec</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results_flat</span> <span class="o">=</span> <span class="n">flatten_results_dict</span><span class="p">(</span>
-    <span class="n">results</span><span class="o">=</span><span class="n">result</span><span class="p">,</span> <span class="n">remove_metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">],</span> <span class="n">model_name</span><span class="o">=</span><span class="s2">&quot;model_for_preds_prob&quot;</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">result</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results_flat</span>
-</pre></div>
-</div>
-</div>
-<p>We first need to process the evaluation results to get the metrics in the right format. The descriptions dictionary will appear as you hover on metrics in the report, so feel free to change them as it’s appropriate for your usage.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">results_flat</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">split</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)</span>  <span class="c1"># noqa: PLW2901</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;BinaryPrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of predicted positive instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryRecall&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAccuracy&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of all instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAveragePrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryF1Score&quot;</span><span class="p">:</span> <span class="s2">&quot;The harmonic mean of precision and recall.&quot;</span><span class="p">,</span>
-    <span class="p">}</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">log_quantitative_analysis</span><span class="p">(</span>
-        <span class="s2">&quot;performance&quot;</span><span class="p">,</span>
-        <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
-        <span class="n">value</span><span class="o">=</span><span class="n">metric</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-        <span class="n">description</span><span class="o">=</span><span class="n">descriptions</span><span class="p">[</span><span class="n">name</span><span class="p">],</span>
-        <span class="n">metric_slice</span><span class="o">=</span><span class="n">split</span><span class="p">,</span>
-        <span class="n">pass_fail_thresholds</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
-        <span class="n">pass_fail_threshold_fns</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">),</span>
-    <span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>We can also use the <code class="docutils literal notranslate"><span class="pre">ClassificationPlotter</span></code>(as demonstrated in Evaluation example) to plot the performance metrics and the add the figure to the model card using the log_plotly_figure method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plotter</span> <span class="o">=</span> <span class="n">ClassificationPlotter</span><span class="p">(</span><span class="n">task_type</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">])</span>
-<span class="n">plotter</span><span class="o">.</span><span class="n">set_template</span><span class="p">(</span><span class="s2">&quot;plotly_white&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># extracting the ROC curves and AUROC results for all the slices</span>
-<span class="n">model_name</span> <span class="o">=</span> <span class="s2">&quot;model_for_preds_prob&quot;</span>
-<span class="n">roc_curves</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">result</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">aurocs</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">result</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-
-<span class="c1"># plotting the ROC curves for all the slices</span>
-<span class="n">roc_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">roc_curve_comparison</span><span class="p">(</span><span class="n">roc_curves</span><span class="p">,</span> <span class="n">aurocs</span><span class="o">=</span><span class="n">aurocs</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">roc_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;ROC Curve for All Patients&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">roc_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the overall classification metric values.</span>
-<span class="n">overall_performance</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-    <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">result</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;overall&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-    <span class="k">if</span> <span class="n">metric_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-<span class="p">}</span>
-<span class="c1"># Plotting the overall classification metric values.</span>
-<span class="n">overall_performance_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_value</span><span class="p">(</span>
-    <span class="n">overall_performance</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Overall Performance&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">overall_performance_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Overall Performance&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">overall_performance_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the metric values for all the slices.</span>
-<span class="n">slice_metrics</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="p">{</span>
-        <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-        <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">slice_results</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">metric_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-    <span class="p">}</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">result</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="c1"># Plotting the metric values for all the slices.</span>
-<span class="n">slice_metrics_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_comparison_bar</span><span class="p">(</span><span class="n">slice_metrics</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">slice_metrics_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Slice Metric Comparison&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">slice_metrics_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Report-Generation">
-<h2>Report Generation<a class="headerlink" href="#Report-Generation" title="Link to this heading">#</a></h2>
-<section id="ModelCard">
-<h3>ModelCard<a class="headerlink" href="#ModelCard" title="Link to this heading">#</a></h3>
-<p>First, let’s document the model details section. A <strong>ModelCard</strong> has several <strong>Sections</strong> and each <strong>Section</strong> includes multiple <strong>Fields</strong>. Model details could be one of the sections in our model card, and it has the following fields by default:</p>
-<ul class="simple">
-<li><p><strong>``description``</strong>: A high-level description of the model and its usage for a general audience.</p></li>
-<li><p><strong>``version``</strong>: The version of the model.</p></li>
-<li><p><strong>``owners``</strong>: The individuals or organizations that own the model.</p></li>
-<li><p><strong>``license``</strong>: The license under which the model is made available.</p></li>
-<li><p><strong>``citation``</strong>: The citation for the model.</p></li>
-<li><p><strong>``references``</strong>: Links to resources that are relevant to the model.</p></li>
-<li><p><strong>``path``</strong>: The path to where the model is stored.</p></li>
-<li><p><strong>``regulatory_requirements``</strong>: The regulatory requirements that are relevant to the model.</p></li>
-</ul>
-<p>We can add additional fields to the model details section by passing a dictionary to the <code class="docutils literal notranslate"><span class="pre">log_from_dict</span></code> method and specifying the section name as <code class="docutils literal notranslate"><span class="pre">model_details</span></code>. You can also use the <code class="docutils literal notranslate"><span class="pre">log_descriptor</span></code> method to add a new field object with a description attribute to any section of the model card.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;Heart Failure Prediction Model&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;The model was trained on the Kaggle Heart Failure </span><span class="se">\</span>
-<span class="s2">        Prediction Dataset to predict risk of heart failure.&quot;</span><span class="p">,</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;model_details&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">report</span><span class="o">.</span><span class="n">log_version</span><span class="p">(</span>
-    <span class="n">version_str</span><span class="o">=</span><span class="s2">&quot;0.0.1&quot;</span><span class="p">,</span>
-    <span class="n">date</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">date</span><span class="o">.</span><span class="n">today</span><span class="p">()),</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Initial Release&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_owner</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;CyclOps Team&quot;</span><span class="p">,</span>
-    <span class="n">contact</span><span class="o">=</span><span class="s2">&quot;vectorinstitute.github.io/cyclops/&quot;</span><span class="p">,</span>
-    <span class="n">email</span><span class="o">=</span><span class="s2">&quot;cyclops@vectorinstitute.ai&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_license</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">&quot;Apache-2.0&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_reference</span><span class="p">(</span>
-    <span class="n">link</span><span class="o">=</span><span class="s2">&quot;https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html&quot;</span><span class="p">,</span>  <span class="c1"># noqa: E501</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Considerations">
-<h3>Considerations<a class="headerlink" href="#Considerations" title="Link to this heading">#</a></h3>
-<p>Next, let’s populate the considerations section, which includes the following fields by default: - <strong>``users``</strong>: The intended users of the model. - <strong>``use_cases``</strong>: The use cases for the model. These could be primary, downstream or out-of-scope use cases. - <strong>``fairness_assessment``</strong>: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms. - <strong>``ethical_considerations``</strong>: The risks associated with using the model and the
-steps taken to mitigate them. This can be populated using the log_risk method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;users&quot;</span><span class="p">:</span> <span class="p">[</span>
-            <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;Hospitals&quot;</span><span class="p">},</span>
-            <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;Clinicians&quot;</span><span class="p">},</span>
-        <span class="p">],</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;considerations&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_user</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;ML Engineers&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_use_case</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Predicting risk of heart failure.&quot;</span><span class="p">,</span>
-    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;primary&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_use_case</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Predicting risk of pathologies and conditions other</span><span class="se">\</span>
-<span class="s2">    than heart failure.&quot;</span><span class="p">,</span>
-    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;out-of-scope&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_fairness_assessment</span><span class="p">(</span>
-    <span class="n">affected_group</span><span class="o">=</span><span class="s2">&quot;sex, age&quot;</span><span class="p">,</span>
-    <span class="n">benefit</span><span class="o">=</span><span class="s2">&quot;Improved health outcomes for patients.&quot;</span><span class="p">,</span>
-    <span class="n">harm</span><span class="o">=</span><span class="s2">&quot;Biased predictions for patients in certain groups (e.g. older patients) </span><span class="se">\</span>
-<span class="s2">        may lead to worse health outcomes.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;We will monitor the performance of the model on these groups </span><span class="se">\</span>
-<span class="s2">        and retrain the model if the performance drops below a certain threshold.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_risk</span><span class="p">(</span>
-    <span class="n">risk</span><span class="o">=</span><span class="s2">&quot;The model may be used to make decisions that affect the health of patients.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;The model should be continuously monitored for performance </span><span class="se">\</span>
-<span class="s2">        and retrained if the performance drops below a certain threshold.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Exporting-report">
-<h3>Exporting report<a class="headerlink" href="#Exporting-report" title="Link to this heading">#</a></h3>
-<p>Once the model card is populated, you can generate the report using the <code class="docutils literal notranslate"><span class="pre">export</span></code> method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named <code class="docutils literal notranslate"><span class="pre">cyclops_reports</span></code> in the current working directory. You can change the path by passing a <code class="docutils literal notranslate"><span class="pre">output_dir</span></code> argument when instantiating the <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> class.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-
-<span class="n">synthetic_timestamps</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span>
-    <span class="n">start</span><span class="o">=</span><span class="s2">&quot;1/1/2020&quot;</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s2">&quot;D&quot;</span>
-<span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
-
-
-<span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">overview</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="n">report_path</span> <span class="o">=</span> <span class="n">report</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
-    <span class="n">output_filename</span><span class="o">=</span><span class="s2">&quot;heart_failure_report_periodic.html&quot;</span><span class="p">,</span>
-    <span class="n">synthetic_timestamp</span><span class="o">=</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-    <span class="n">last_n_evals</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
-<span class="n">metric_save</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))):</span>
-    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-        <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">quantitative_analysis</span><span class="o">.</span><span class="n">performance_metrics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="n">metric_save</span><span class="p">,</span>
-        <span class="p">)</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">overview</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">quantitative_analysis</span><span class="o">.</span><span class="n">performance_metrics</span><span class="p">:</span>
-        <span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span>
-            <span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span>
-            <span class="mi">0</span><span class="p">,</span>
-            <span class="mi">1</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">metric</span><span class="o">.</span><span class="n">tests</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">passed</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">&gt;=</span> <span class="mf">0.7</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="n">metrics</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">quantitative_analysis</span><span class="o">.</span><span class="n">performance_metrics</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">metric</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;BinaryAccuracy&quot;</span> <span class="ow">and</span> <span class="n">metric</span><span class="o">.</span><span class="n">slice</span> <span class="o">==</span> <span class="s2">&quot;Age:[30 - 50)&quot;</span><span class="p">:</span>
-                <span class="n">metric_save</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">metric</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">metrics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">metric</span><span class="p">)</span>
-        <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">quantitative_analysis</span><span class="o">.</span><span class="n">performance_metrics</span> <span class="o">=</span> <span class="n">metrics</span>
-    <span class="n">report_path</span> <span class="o">=</span> <span class="n">report</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
-        <span class="n">output_filename</span><span class="o">=</span><span class="s2">&quot;heart_failure_report_periodic.html&quot;</span><span class="p">,</span>
-        <span class="n">synthetic_timestamp</span><span class="o">=</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span>
-        <span class="n">last_n_evals</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
-<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="s2">&quot;./cyclops_report&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>You’re now able to view the report <a class="reference external" href="./heart_failure_report_periodic.html">report</a>.</p>
-</section>
-</section>
-</section>
-
-        </article>
-      </div>
-      <footer>
-        
-        <div class="related-pages">
-          <a class="next-page" href="../monitoring.html">
-              <div class="page-info">
-                <div class="context">
-                  <span>Next</span>
-                </div>
-                <div class="title">Monitoring</div>
-              </div>
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-            </a>
-          <a class="prev-page" href="../model_report.html">
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-              <div class="page-info">
-                <div class="context">
-                  <span>Previous</span>
-                </div>
-                
-                <div class="title">Model Report</div>
-                
-              </div>
-            </a>
-        </div>
-        <div class="bottom-of-page">
-          <div class="left-details">
-            <div class="copyright">
-                Copyright &#169; 2024, Vector AI Engineering
-            </div>
-            <!--
-            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-            
-            <a href="https://github.com/pradyunsg/furo">Furo</a>
-            
-            -->
-          </div>
-          <div class="right-details">
-            <div class="icons">
-              <a class="muted-link " href="https://github.com/VectorInstitute/cyclops" aria-label="GitHub">
-                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
-                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
-                </svg>
-            </a>
-              
-            </div>
-          </div>
-        </div>
-        <div class="footer-container" style="text-align: center;">
-          <div class="footer-small-text">
-              <br> All <a href="https://github.com/VectorInstitute/cyclops">source code for cyclops</a> is freely available under the terms of an <a href="https://opensource.org/licenses/Apache-2.0">Apache-2.0</a> license.
-          </div>
-
-          <div class="footer-small-text">
-            This work is made possible due to the data obtained from the <a href="https://www.geminimedicine.ca/">GEMINI study</a>.
-          </div>
-
-          <div class="logo">
-            <a class="logo" href="https://vectorinstitute.ai/">
-              <span class="logo"></span>
-              <img alt="Logo of the Vector Institute" style="width:90px; max-width:90px;" src="../_static/logos/vector_logo.png" />
-            </a>
-            <a class="logo" href="https://www.geminimedicine.ca/">
-              <span class="logo"></span>
-              <img alt="Logo of GEMINI" style="width:90px; max-width:90px;" src="../_static/logos/gemini_logo.png" />
-            </a>
-          </div>
-        </div>
-        
-      </footer>
-    </div>
-    <aside class="toc-drawer">
-      
-      
-      <div class="toc-sticky toc-scroll">
-        <div class="toc-title-container">
-          <span class="toc-title">
-            On this page
-          </span>
-        </div>
-        <div class="toc-tree-container">
-          <div class="toc-tree">
-            <ul>
-<li><a class="reference internal" href="#">Report Generation for Heart Failure Prediction</a><ul>
-<li><a class="reference internal" href="#Create-Model-Card-Report">Create Model Card Report</a></li>
-<li><a class="reference internal" href="#Data-Loading">Data Loading</a></li>
-<li><a class="reference internal" href="#Adding-figures-to-report">Adding figures to report</a><ul>
-<li><a class="reference internal" href="#Age-distribution-figure">Age distribution figure</a></li>
-<li><a class="reference internal" href="#Outcome-distribution">Outcome distribution</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#Data-Preprocessing">Data Preprocessing</a></li>
-<li><a class="reference internal" href="#Create-HuggingFace-Dataset">Create HuggingFace Dataset</a></li>
-<li><a class="reference internal" href="#Training">Training</a></li>
-<li><a class="reference internal" href="#Evaluation">Evaluation</a></li>
-<li><a class="reference internal" href="#Data-Slicing">Data Slicing</a><ul>
-<li><a class="reference internal" href="#Age-distribution-in-test-data">Age distribution in test data</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#Logging-metrics-and-results-to-report">Logging metrics and results to report</a></li>
-<li><a class="reference internal" href="#Report-Generation">Report Generation</a><ul>
-<li><a class="reference internal" href="#ModelCard">ModelCard</a></li>
-<li><a class="reference internal" href="#Considerations">Considerations</a></li>
-<li><a class="reference internal" href="#Exporting-report">Exporting report</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-          </div>
-        </div>
-      </div>
-      
-      
-    </aside>
-  </div>
-</div></body>
-</html>
\ No newline at end of file
diff --git a/api/examples/report.ipynb b/api/examples/report.ipynb
deleted file mode 100644
index 6312ba6f5..000000000
--- a/api/examples/report.ipynb
+++ /dev/null
@@ -1,900 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import copy\n",
-    "import inspect\n",
-    "import os\n",
-    "import shutil\n",
-    "from datetime import date\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "from datasets import Dataset\n",
-    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate.metrics import create_metric\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.report import ModelCardReport\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter\n",
-    "from cyclops.report.utils import flatten_results_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Report Generation for Heart Failure Prediction\n",
-    "Here's an example to demonstrate how we can generate a report as we proceed through all the steps to train and evaluate a model. For this purpose, we are going to use Kaggle's heart prediction failure dataset and gradually populate the report with information about dataset, model and results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Model Card Report\n",
-    "First, we should create a `ModelCardReport` object to fill in the fields and sections after training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Constants\n",
-    "DATA_DIR = \"./data\"\n",
-    "RANDOM_SEED = 21"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Loading\n",
-    "Before starting, make sure to install the Kaggle API by running pip install kaggle. To use the Kaggle API, you need to sign up for a Kaggle account at https://www.kaggle.com. Then go to the ‘Account’ tab of your user profile (https://www.kaggle.com/<username>/account) and select ‘Create API Token’. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location ~/.kaggle/kaggle.json on your machine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "api = KaggleApi()\n",
-    "api.authenticate()\n",
-    "api.dataset_download_files(\n",
-    "    \"fedesoriano/heart-failure-prediction\",\n",
-    "    path=DATA_DIR,\n",
-    "    unzip=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(os.path.join(DATA_DIR, \"heart.csv\"))\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.describe().T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"Sex\")\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    title=\"Sex Distribution\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adding figures to report\n",
-    "We can add figures and diagrams to report. We can define caption and the section of the report that this figure belongs to. Since we are exploring the distribution of different features in the dataset, we add it to `datasets` section:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Sex Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Age distribution figure\n",
-    "We plot a histogram of ages similarly and add the figure to our report. This will appear under **Dataset** section:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(df, x=\"Age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Outcome distribution\n",
-    "Plot outcome distribution and add it to report. Similarly, we also place it under **Dataset** section:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"outcome\"] = df[\"HeartDisease\"].astype(\"int\")\n",
-    "df = df.drop(columns=[\"HeartDisease\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"outcome\")\n",
-    "fig.update_traces(textinfo=\"percent+label\")\n",
-    "fig.update_layout(title_text=\"Outcome Distribution\")\n",
-    "fig.update_traces(\n",
-    "    hovertemplate=\"Outcome: %{label}<br>Count: \\\n",
-    "    %{value}<br>Percent: %{percent}\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Outcome Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_counts = df[\"outcome\"].value_counts()\n",
-    "class_ratio = class_counts[0] / class_counts[1]\n",
-    "print(class_ratio, class_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This dataset does not have any null values, so we can jump to feature scaling. The string data in the dataframe is in the form of object, we need to convert it back to string to work on it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "string_col = df.select_dtypes(include=\"object\").columns\n",
-    "df[string_col] = df[string_col].astype(\"string\")\n",
-    "string_col = df.select_dtypes(\"string\").columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are going to apply tree-based models to our data, so we use `LabelEncoder`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target = \"outcome\"\n",
-    "\n",
-    "df_processed = pd.get_dummies(df, columns=string_col, drop_first=False)\n",
-    "df_processed.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_cols = df_processed.columns.to_list()\n",
-    "feature_cols.remove(target)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before training, let's document dataset in the model card.\n",
-    "This can be done using the log_dataset method, which takes the following arguments: \n",
-    "\n",
-    "- `description`: A description of the dataset. \n",
-    "- `citation`: The citation for the dataset. \n",
-    "- `link`: A link to a resource for the dataset. \n",
-    "- `license_id`: The SPDX license identifier for the dataset. \n",
-    "- `version`: The version of the dataset. \n",
-    "- `features`: A list of features in the dataset. \n",
-    "- `split`: The split of the dataset (train, test, validation, etc.). \n",
-    "- `sensitive_features`: A list of sensitive features used to train/evaluate the model. \n",
-    "- `sensitive_feature_justification`: A justification for the sensitive features used to train/evaluate the model.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_dataset(\n",
-    "    description=\"\"\"This dataset was created by combining different datasets\n",
-    "    already available independently but not combined before. In this dataset,\n",
-    "    5 heart datasets are combined over 11 common features. Every dataset used\n",
-    "    can be found under the Index of heart disease datasets from UCI\n",
-    "    Machine Learning Repository on the following link:\n",
-    "    https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/.\"\"\",\n",
-    "    citation=inspect.cleandoc(\n",
-    "        \"\"\"\n",
-    "        @misc{fedesoriano,\n",
-    "          title={Heart Failure Prediction Dataset.},\n",
-    "          author={Fedesoriano, F},\n",
-    "          year={2021},\n",
-    "          publisher={Kaggle}\n",
-    "        }\n",
-    "    \"\"\",\n",
-    "    ),\n",
-    "    link=\"\"\"\n",
-    "    https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction\n",
-    "    \"\"\",\n",
-    "    license_id=\"CC0-1.0\",\n",
-    "    version=\"Version 1\",\n",
-    "    features=df.columns.to_list().remove(target),\n",
-    "    sensitive_features=[\"Sex\", \"Age\"],\n",
-    "    sensitive_feature_justification=\"Demographic information like age and gender \\\n",
-    "        often have a strong correlation with health outcomes. For example, older \\\n",
-    "        patients are more likely to have a higher risk of heart disease.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create HuggingFace Dataset\n",
-    "We convert our processed Pandas dataframe into a Hugging Face dataset, for later evaluation by Cyclop metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset = Dataset.from_pandas(df_processed)\n",
-    "dataset.cleanup_cache_files()\n",
-    "print(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Split dataframe into inputs and outputs\n",
-    "X, y = df_processed[feature_cols], df_processed[target]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Splitting into train and test\n",
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    X,\n",
-    "    y,\n",
-    "    test_size=0.2,\n",
-    "    random_state=RANDOM_SEED,\n",
-    ")\n",
-    "classifier = LogisticRegression()\n",
-    "scaler = MinMaxScaler()\n",
-    "X_train = scaler.fit_transform(X_train)\n",
-    "X_test = scaler.fit_transform(X_test)\n",
-    "classifier.fit(X_train, y_train)\n",
-    "y_pred = classifier.predict(X_test)\n",
-    "y_pred_prob = classifier.predict_proba(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluation\n",
-    "As demonstrated in evaluation tutorial, we define a metric dict:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_roc_curve\",\n",
-    "    \"binary_auroc\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)\n",
-    "metric_collection(y_test.values, np.float64(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Slicing\n",
-    "In addition to overall metrics, it might be useful to see how the model performs on certain subpopulation or subsets. We can define these subsets using SliceSpec objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"Age\": {\n",
-    "            \"min_value\": 30,\n",
-    "            \"max_value\": 50,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"Age\": {\n",
-    "            \"min_value\": 50,\n",
-    "            \"max_value\": 70,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below, we are combining the raw features of the test data and the predictions so that we use them later for slice-specific evaluations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get positions of matching indices in df\n",
-    "matching_positions = y_test.index.get_indexer(df.index)\n",
-    "\n",
-    "# Select rows from df using matching positions (valid positions are non-negative)\n",
-    "df_test = df.iloc[matching_positions[matching_positions >= 0]]\n",
-    "df_test[\"preds\"] = y_pred\n",
-    "df_test[\"preds_prob\"] = y_pred_prob[:, 1]\n",
-    "df_test.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Age distribution in test data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(df_test, x=\"Age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution in Test Data\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Logging metrics and results to report\n",
-    "Here, we gather evalutaions and add them to the report.\n",
-    "\n",
-    "We can add a performance metric to the model card using the `log_performance_metric` method, which expects a dictionary where the keys are in the following format: `slice_name/metric_name`. For instance, `overall/accuracy` or `Age:[30 - 50)/BinaryPrecision`.\n",
-    "\n",
-    "We first need to process the evaluation results to get the metrics in the right format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from cyclops.evaluate import evaluator\n",
-    "\n",
-    "\n",
-    "# Create Dataset object\n",
-    "heart_failure_data = Dataset.from_pandas(df_test)\n",
-    "\n",
-    "result = evaluator.evaluate(\n",
-    "    dataset=heart_failure_data,\n",
-    "    metrics=metric_collection,  # type: ignore[list-item]\n",
-    "    target_columns=target,\n",
-    "    prediction_columns=\"preds_prob\",\n",
-    "    slice_spec=slice_spec,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = flatten_results_dict(\n",
-    "    results=result, remove_metrics=[\"BinaryROC\"], model_name=\"model_for_preds_prob\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We first need to process the evaluation results to get the metrics in the right format. The descriptions dictionary will appear as you hover on metrics in the report, so feel free to change them as it's appropriate for your usage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the `ClassificationPlotter`(as demonstrated in Evaluation example) to plot the performance metrics and the add the figure to the model card using the log_plotly_figure method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the ROC curves and AUROC results for all the slices\n",
-    "model_name = \"model_for_preds_prob\"\n",
-    "roc_curves = {\n",
-    "    slice_name: slice_results[\"BinaryROC\"]\n",
-    "    for slice_name, slice_results in result[model_name].items()\n",
-    "}\n",
-    "aurocs = {\n",
-    "    slice_name: slice_results[\"BinaryAUROC\"]\n",
-    "    for slice_name, slice_results in result[model_name].items()\n",
-    "}\n",
-    "\n",
-    "# plotting the ROC curves for all the slices\n",
-    "roc_plot = plotter.roc_curve_comparison(roc_curves, aurocs=aurocs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=roc_plot,\n",
-    "    caption=\"ROC Curve for All Patients\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "roc_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the overall classification metric values.\n",
-    "overall_performance = {\n",
-    "    metric_name: metric_value\n",
-    "    for metric_name, metric_value in result[model_name][\"overall\"].items()\n",
-    "    if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "}\n",
-    "# Plotting the overall classification metric values.\n",
-    "overall_performance_plot = plotter.metrics_value(\n",
-    "    overall_performance,\n",
-    "    title=\"Overall Performance\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=overall_performance_plot,\n",
-    "    caption=\"Overall Performance\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "overall_performance_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in result[model_name].items()\n",
-    "}\n",
-    "# Plotting the metric values for all the slices.\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=slice_metrics_plot,\n",
-    "    caption=\"Slice Metric Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Report Generation\n",
-    "\n",
-    "### ModelCard\n",
-    "First, let's document the model details section. A **ModelCard** has several **Sections** and each **Section** includes multiple **Fields**. Model details could be one of the sections in our model card, and it has the following fields by default:\n",
-    "\n",
-    "- **`description`**: A high-level description of the model and its usage for a general audience. \n",
-    "- **`version`**: The version of the model. \n",
-    "- **`owners`**: The individuals or organizations that own the model. \n",
-    "- **`license`**: The license under which the model is made available. \n",
-    "- **`citation`**: The citation for the model. \n",
-    "- **`references`**: Links to resources that are relevant to the model. \n",
-    "- **`path`**: The path to where the model is stored. \n",
-    "- **`regulatory_requirements`**: The regulatory requirements that are relevant to the model.\n",
-    "\n",
-    "We can add additional fields to the model details section by passing a dictionary to the `log_from_dict` method and specifying the section name as `model_details`. You can also use the `log_descriptor` method to add a new field object with a description attribute to any section of the model card."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"Heart Failure Prediction Model\",\n",
-    "        \"description\": \"The model was trained on the Kaggle Heart Failure \\\n",
-    "        Prediction Dataset to predict risk of heart failure.\",\n",
-    "    },\n",
-    "    section_name=\"model_details\",\n",
-    ")\n",
-    "\n",
-    "report.log_version(\n",
-    "    version_str=\"0.0.1\",\n",
-    "    date=str(date.today()),\n",
-    "    description=\"Initial Release\",\n",
-    ")\n",
-    "report.log_owner(\n",
-    "    name=\"CyclOps Team\",\n",
-    "    contact=\"vectorinstitute.github.io/cyclops/\",\n",
-    "    email=\"cyclops@vectorinstitute.ai\",\n",
-    ")\n",
-    "report.log_license(identifier=\"Apache-2.0\")\n",
-    "report.log_reference(\n",
-    "    link=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\",  # noqa: E501\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Considerations\n",
-    "\n",
-    "Next, let’s populate the considerations section, which includes the following fields by default: \n",
-    "- **`users`**: The intended users of the model. \n",
-    "- **`use_cases`**: The use cases for the model. These could be primary, downstream or out-of-scope use cases. \n",
-    "- **`fairness_assessment`**: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms. \n",
-    "- **`ethical_considerations`**: The risks associated with using the model and the steps taken to mitigate them. This can be populated using the log_risk method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"users\": [\n",
-    "            {\"description\": \"Hospitals\"},\n",
-    "            {\"description\": \"Clinicians\"},\n",
-    "        ],\n",
-    "    },\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_user(description=\"ML Engineers\")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of heart failure.\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of pathologies and conditions other\\\n",
-    "    than heart failure.\",\n",
-    "    kind=\"out-of-scope\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"sex, age\",\n",
-    "    benefit=\"Improved health outcomes for patients.\",\n",
-    "    harm=\"Biased predictions for patients in certain groups (e.g. older patients) \\\n",
-    "        may lead to worse health outcomes.\",\n",
-    "    mitigation_strategy=\"We will monitor the performance of the model on these groups \\\n",
-    "        and retrain the model if the performance drops below a certain threshold.\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"The model may be used to make decisions that affect the health of patients.\",\n",
-    "    mitigation_strategy=\"The model should be continuously monitored for performance \\\n",
-    "        and retrained if the performance drops below a certain threshold.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exporting report\n",
-    "Once the model card is populated, you can generate the report using the `export` method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named `cyclops_reports` in the current working directory. You can change the path by passing a `output_dir` argument when instantiating the `ModelCardReport` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(42)\n",
-    "\n",
-    "synthetic_timestamps = pd.date_range(\n",
-    "    start=\"1/1/2020\", periods=10, freq=\"D\"\n",
-    ").values.astype(str)\n",
-    "\n",
-    "\n",
-    "report._model_card.overview = None\n",
-    "report_path = report.export(\n",
-    "    output_filename=\"heart_failure_report_periodic.html\",\n",
-    "    synthetic_timestamp=synthetic_timestamps[0],\n",
-    "    last_n_evals=3,\n",
-    ")\n",
-    "\n",
-    "shutil.copy(f\"{report_path}\", \".\")\n",
-    "metric_save = None\n",
-    "for i in tqdm(range(len(synthetic_timestamps[1:]))):\n",
-    "    if i == 3:\n",
-    "        report._model_card.quantitative_analysis.performance_metrics.append(\n",
-    "            metric_save,\n",
-    "        )\n",
-    "    report._model_card.overview = None\n",
-    "    for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "        metric.value = np.clip(\n",
-    "            metric.value + np.random.normal(0, 0.1),\n",
-    "            0,\n",
-    "            1,\n",
-    "        )\n",
-    "        metric.tests[0].passed = bool(metric.value >= 0.7)\n",
-    "    if i == 2:\n",
-    "        metrics = []\n",
-    "        for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "            if metric.type == \"BinaryAccuracy\" and metric.slice == \"Age:[30 - 50)\":\n",
-    "                metric_save = copy.deepcopy(metric)\n",
-    "            else:\n",
-    "                metrics.append(metric)\n",
-    "        report._model_card.quantitative_analysis.performance_metrics = metrics\n",
-    "    report_path = report.export(\n",
-    "        output_filename=\"heart_failure_report_periodic.html\",\n",
-    "        synthetic_timestamp=synthetic_timestamps[i + 1],\n",
-    "        last_n_evals=3,\n",
-    "    )\n",
-    "    shutil.copy(f\"{report_path}\", \".\")\n",
-    "shutil.rmtree(\"./cyclops_report\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You're now able to view the report [report](./heart_failure_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cyclops",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/api/genindex.html b/api/genindex.html
index 1203bc540..82e9acf88 100644
--- a/api/genindex.html
+++ b/api/genindex.html
@@ -58,7 +58,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -189,24 +188,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/index.html b/api/index.html
index 31da1bd24..c63cc5064 100644
--- a/api/index.html
+++ b/api/index.html
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/installation.html b/api/installation.html
index 690c929de..0064b3533 100644
--- a/api/installation.html
+++ b/api/installation.html
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 current has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l2 current current-page"><a class="current reference internal" href="#">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/model_report.html b/api/model_report.html
index 7b58f3302..5648eb4d4 100644
--- a/api/model_report.html
+++ b/api/model_report.html
@@ -7,7 +7,7 @@
 
     
       <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Report Generation for Heart Failure Prediction" href="examples/report.html" /><link rel="prev" title="Breast Cancer Classification and Evaluation" href="examples/metrics.html" />
+    <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Monitoring" href="monitoring.html" /><link rel="prev" title="Evaluation" href="evaluation.html" />
 
     <link rel="shortcut icon" href="_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
 
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 current has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 current has-children current-page"><a class="current reference internal" href="#">Model Report</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 current has-children current-page"><a class="current reference internal" href="#">Model Report</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
@@ -407,33 +399,6 @@ <h2>Considerations<a class="headerlink" href="#considerations" title="Link to th
 <img alt="https://github.com/VectorInstitute/cyclops/assets/8986523/402f2e3c-a68e-484d-bd1f-ee458d15d45c" src="https://github.com/VectorInstitute/cyclops/assets/8986523/402f2e3c-a68e-484d-bd1f-ee458d15d45c" />
 <p>Follow the example below for the instructions on how to generate a model report:</p>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="examples/report.html#Create-Model-Card-Report">Create Model Card Report</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/report.html#Data-Loading">Data Loading</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/report.html#Adding-figures-to-report">Adding figures to report</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html#Age-distribution-figure">Age distribution figure</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html#Outcome-distribution">Outcome distribution</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/report.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/report.html#Create-HuggingFace-Dataset">Create HuggingFace Dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/report.html#Training">Training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/report.html#Evaluation">Evaluation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/report.html#Data-Slicing">Data Slicing</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html#Age-distribution-in-test-data">Age distribution in test data</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="examples/report.html#Logging-metrics-and-results-to-report">Logging metrics and results to report</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/report.html#Report-Generation">Report Generation</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html#ModelCard">ModelCard</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html#Considerations">Considerations</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html#Exporting-report">Exporting report</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
 </div>
 </section>
 </section>
@@ -443,23 +408,23 @@ <h2>Considerations<a class="headerlink" href="#considerations" title="Link to th
       <footer>
         
         <div class="related-pages">
-          <a class="next-page" href="examples/report.html">
+          <a class="next-page" href="monitoring.html">
               <div class="page-info">
                 <div class="context">
                   <span>Next</span>
                 </div>
-                <div class="title">Report Generation for Heart Failure Prediction</div>
+                <div class="title">Monitoring</div>
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
-          <a class="prev-page" href="examples/metrics.html">
+          <a class="prev-page" href="evaluation.html">
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
               <div class="page-info">
                 <div class="context">
                   <span>Previous</span>
                 </div>
                 
-                <div class="title">Breast Cancer Classification and Evaluation</div>
+                <div class="title">Evaluation</div>
                 
               </div>
             </a>
diff --git a/api/monitoring.html b/api/monitoring.html
index 380209d7f..83b2ca85b 100644
--- a/api/monitoring.html
+++ b/api/monitoring.html
@@ -7,7 +7,7 @@
 
     
       <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Tutorials" href="tutorials.html" /><link rel="prev" title="Report Generation for Heart Failure Prediction" href="examples/report.html" />
+    <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Tutorials" href="tutorials.html" /><link rel="prev" title="Model Report" href="model_report.html" />
 
     <link rel="shortcut icon" href="_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
 
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 current has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2 current current-page"><a class="current reference internal" href="#">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
@@ -371,14 +363,14 @@ <h2>Subgroup Performance<a class="headerlink" href="#subgroup-performance" title
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
-          <a class="prev-page" href="examples/report.html">
+          <a class="prev-page" href="model_report.html">
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
               <div class="page-info">
                 <div class="context">
                   <span>Previous</span>
                 </div>
                 
-                <div class="title">Report Generation for Heart Failure Prediction</div>
+                <div class="title">Model Report</div>
                 
               </div>
             </a>
diff --git a/api/objects.inv b/api/objects.inv
index 1abd269ad..a8fa3126e 100644
Binary files a/api/objects.inv and b/api/objects.inv differ
diff --git a/api/page.html b/api/page.html
index e93301fb8..262393b2f 100644
--- a/api/page.html
+++ b/api/page.html
@@ -60,7 +60,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -191,24 +190,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/py-modindex.html b/api/py-modindex.html
index d6b0fb596..2b96c6abc 100644
--- a/api/py-modindex.html
+++ b/api/py-modindex.html
@@ -58,7 +58,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -189,24 +188,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html b/api/reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html
index 58410f6e4..d510fdcbe 100644
--- a/api/reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html
+++ b/api/reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.aggregate.html b/api/reference/api/_autosummary/cyclops.data.aggregate.html
index 71f332558..b94c90ac8 100644
--- a/api/reference/api/_autosummary/cyclops.data.aggregate.html
+++ b/api/reference/api/_autosummary/cyclops.data.aggregate.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html b/api/reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html
index 6dae832e8..229fc1796 100644
--- a/api/reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html
+++ b/api/reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html b/api/reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html
index ec6d6f24f..d15293ccc 100644
--- a/api/reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html
+++ b/api/reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html b/api/reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html
index f42590a95..13646922a 100644
--- a/api/reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html
+++ b/api/reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.features.medical_image.html b/api/reference/api/_autosummary/cyclops.data.features.medical_image.html
index a32eb8122..bf95b3500 100644
--- a/api/reference/api/_autosummary/cyclops.data.features.medical_image.html
+++ b/api/reference/api/_autosummary/cyclops.data.features.medical_image.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html b/api/reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html
index c5c96e057..80ca946a1 100644
--- a/api/reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html
+++ b/api/reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.slicer.compound_filter.html b/api/reference/api/_autosummary/cyclops.data.slicer.compound_filter.html
index 6f948859e..aabc8bfb8 100644
--- a/api/reference/api/_autosummary/cyclops.data.slicer.compound_filter.html
+++ b/api/reference/api/_autosummary/cyclops.data.slicer.compound_filter.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html b/api/reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html
index e16286f46..814bcf366 100644
--- a/api/reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html
+++ b/api/reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html b/api/reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html
index 9a2b5fad4..967e7a518 100644
--- a/api/reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html
+++ b/api/reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.slicer.filter_range.html b/api/reference/api/_autosummary/cyclops.data.slicer.filter_range.html
index 27d44dc8d..404e401c8 100644
--- a/api/reference/api/_autosummary/cyclops.data.slicer.filter_range.html
+++ b/api/reference/api/_autosummary/cyclops.data.slicer.filter_range.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html b/api/reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html
index 719f9eea0..11bfefccf 100644
--- a/api/reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html
+++ b/api/reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.slicer.filter_value.html b/api/reference/api/_autosummary/cyclops.data.slicer.filter_value.html
index 1b0bcec21..04324656e 100644
--- a/api/reference/api/_autosummary/cyclops.data.slicer.filter_value.html
+++ b/api/reference/api/_autosummary/cyclops.data.slicer.filter_value.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.slicer.html b/api/reference/api/_autosummary/cyclops.data.slicer.html
index 7547af0c5..425f316c9 100644
--- a/api/reference/api/_autosummary/cyclops.data.slicer.html
+++ b/api/reference/api/_autosummary/cyclops.data.slicer.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.slicer.is_datetime.html b/api/reference/api/_autosummary/cyclops.data.slicer.is_datetime.html
index 6ee485ee2..e43e763e3 100644
--- a/api/reference/api/_autosummary/cyclops.data.slicer.is_datetime.html
+++ b/api/reference/api/_autosummary/cyclops.data.slicer.is_datetime.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.data.slicer.overall.html b/api/reference/api/_autosummary/cyclops.data.slicer.overall.html
index 5b0ede4c9..413bcc925 100644
--- a/api/reference/api/_autosummary/cyclops.data.slicer.overall.html
+++ b/api/reference/api/_autosummary/cyclops.data.slicer.overall.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html b/api/reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html
index fbc90cdd9..bfd6d2f78 100644
--- a/api/reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html
+++ b/api/reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.monitor.clinical_applicator.html b/api/reference/api/_autosummary/cyclops.monitor.clinical_applicator.html
index 70057cfce..ffeaa2d90 100644
--- a/api/reference/api/_autosummary/cyclops.monitor.clinical_applicator.html
+++ b/api/reference/api/_autosummary/cyclops.monitor.clinical_applicator.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html
index 29db26cf3..b2c23a1f4 100644
--- a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html
+++ b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html
index 3c0fa2e0d..7bbfa1ef8 100644
--- a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html
+++ b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html
index 75dfa9058..00e10133b 100644
--- a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html
+++ b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html
index 6795ab72c..86e690f1a 100644
--- a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html
+++ b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html
index d2102344c..14caa9764 100644
--- a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html
+++ b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html
index 578981bcd..3a1937320 100644
--- a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html
+++ b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html
index e70033978..2e5e40941 100644
--- a/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html
+++ b/api/reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.report.report.ModelCardReport.html b/api/reference/api/_autosummary/cyclops.report.report.ModelCardReport.html
index 8960c79a7..462073dd2 100644
--- a/api/reference/api/_autosummary/cyclops.report.report.ModelCardReport.html
+++ b/api/reference/api/_autosummary/cyclops.report.report.ModelCardReport.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.report.report.html b/api/reference/api/_autosummary/cyclops.report.report.html
index 2856c33e0..49cff8b6d 100644
--- a/api/reference/api/_autosummary/cyclops.report.report.html
+++ b/api/reference/api/_autosummary/cyclops.report.report.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html b/api/reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html
index 7d93a3ed9..5ae4384ac 100644
--- a/api/reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html
+++ b/api/reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html b/api/reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html
index 413f34d9c..478b70e01 100644
--- a/api/reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html
+++ b/api/reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/_autosummary/cyclops.tasks.classification.html b/api/reference/api/_autosummary/cyclops.tasks.classification.html
index 3024584c1..827436cdc 100644
--- a/api/reference/api/_autosummary/cyclops.tasks.classification.html
+++ b/api/reference/api/_autosummary/cyclops.tasks.classification.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/cyclops.data.html b/api/reference/api/cyclops.data.html
index 59f8951a3..594383843 100644
--- a/api/reference/api/cyclops.data.html
+++ b/api/reference/api/cyclops.data.html
@@ -61,7 +61,6 @@
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/require.min.js?v=53367eec"></script>
     <script src="../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/cyclops.monitor.html b/api/reference/api/cyclops.monitor.html
index f261b625e..6fe4b86fc 100644
--- a/api/reference/api/cyclops.monitor.html
+++ b/api/reference/api/cyclops.monitor.html
@@ -61,7 +61,6 @@
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/require.min.js?v=53367eec"></script>
     <script src="../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/cyclops.report.html b/api/reference/api/cyclops.report.html
index b1cdc2dae..0f4249488 100644
--- a/api/reference/api/cyclops.report.html
+++ b/api/reference/api/cyclops.report.html
@@ -61,7 +61,6 @@
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/require.min.js?v=53367eec"></script>
     <script src="../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/cyclops.tasks.html b/api/reference/api/cyclops.tasks.html
index 4484f651b..20720f86a 100644
--- a/api/reference/api/cyclops.tasks.html
+++ b/api/reference/api/cyclops.tasks.html
@@ -61,7 +61,6 @@
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/require.min.js?v=53367eec"></script>
     <script src="../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/evaluator.html b/api/reference/api/evaluator.html
index 66a5e4d6d..e86bb09da 100644
--- a/api/reference/api/evaluator.html
+++ b/api/reference/api/evaluator.html
@@ -61,7 +61,6 @@
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/require.min.js?v=53367eec"></script>
     <script src="../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/fairness_evaluator.html b/api/reference/api/fairness_evaluator.html
index c1144c967..a8a17c50a 100644
--- a/api/reference/api/fairness_evaluator.html
+++ b/api/reference/api/fairness_evaluator.html
@@ -61,7 +61,6 @@
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/require.min.js?v=53367eec"></script>
     <script src="../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics.html b/api/reference/api/metrics.html
index 1031b0f02..2a5fff939 100644
--- a/api/reference/api/metrics.html
+++ b/api/reference/api/metrics.html
@@ -61,7 +61,6 @@
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/require.min.js?v=53367eec"></script>
     <script src="../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/accuracy.html b/api/reference/api/metrics/accuracy.html
index 07154fd2b..1853cbe1a 100644
--- a/api/reference/api/metrics/accuracy.html
+++ b/api/reference/api/metrics/accuracy.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/auroc.html b/api/reference/api/metrics/auroc.html
index 940734f8e..03434ec02 100644
--- a/api/reference/api/metrics/auroc.html
+++ b/api/reference/api/metrics/auroc.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/average_precision.html b/api/reference/api/metrics/average_precision.html
index 00a1474e7..433494cc9 100644
--- a/api/reference/api/metrics/average_precision.html
+++ b/api/reference/api/metrics/average_precision.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/f1score.html b/api/reference/api/metrics/f1score.html
index 3f30655b5..002f9a43e 100644
--- a/api/reference/api/metrics/f1score.html
+++ b/api/reference/api/metrics/f1score.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/f_beta.html b/api/reference/api/metrics/f_beta.html
index 2b0f7040a..708d4d494 100644
--- a/api/reference/api/metrics/f_beta.html
+++ b/api/reference/api/metrics/f_beta.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/precision.html b/api/reference/api/metrics/precision.html
index dae737213..580a7e67a 100644
--- a/api/reference/api/metrics/precision.html
+++ b/api/reference/api/metrics/precision.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/precision_recall_curve.html b/api/reference/api/metrics/precision_recall_curve.html
index 28d37f2d3..a806e8c32 100644
--- a/api/reference/api/metrics/precision_recall_curve.html
+++ b/api/reference/api/metrics/precision_recall_curve.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/recall.html b/api/reference/api/metrics/recall.html
index a0d1f4608..b2e4fd0c8 100644
--- a/api/reference/api/metrics/recall.html
+++ b/api/reference/api/metrics/recall.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/roc.html b/api/reference/api/metrics/roc.html
index 8e24ab215..d46d00ab3 100644
--- a/api/reference/api/metrics/roc.html
+++ b/api/reference/api/metrics/roc.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/sensitivity.html b/api/reference/api/metrics/sensitivity.html
index edea25e2a..27e6b2789 100644
--- a/api/reference/api/metrics/sensitivity.html
+++ b/api/reference/api/metrics/sensitivity.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/specificity.html b/api/reference/api/metrics/specificity.html
index 2d0668faf..9f3c5e8a7 100644
--- a/api/reference/api/metrics/specificity.html
+++ b/api/reference/api/metrics/specificity.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/reference/api/metrics/stat_scores.html b/api/reference/api/metrics/stat_scores.html
index f51119b3a..bab615733 100644
--- a/api/reference/api/metrics/stat_scores.html
+++ b/api/reference/api/metrics/stat_scores.html
@@ -61,7 +61,6 @@
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="../../../_static/copybutton.js?v=ccdb6887"></script>
     <script src="../../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../../_static/require.min.js?v=53367eec"></script>
     <script src="../../../_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/search.html b/api/search.html
index bb53db4f4..ad3945d58 100644
--- a/api/search.html
+++ b/api/search.html
@@ -57,7 +57,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -190,24 +189,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/searchindex.js b/api/searchindex.js
index 59cb8377e..d13d17f82 100644
--- a/api/searchindex.js
+++ b/api/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["api", "contributing", "developing", "evaluation", "examples/metrics", "examples/report", "index", "installation", "model_report", "monitoring", "reference/api/_autosummary/cyclops.data.aggregate", "reference/api/_autosummary/cyclops.data.aggregate.Aggregator", "reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated", "reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg", "reference/api/_autosummary/cyclops.data.features.medical_image", "reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage", "reference/api/_autosummary/cyclops.data.slicer", "reference/api/_autosummary/cyclops.data.slicer.SliceSpec", "reference/api/_autosummary/cyclops.data.slicer.compound_filter", "reference/api/_autosummary/cyclops.data.slicer.filter_datetime", "reference/api/_autosummary/cyclops.data.slicer.filter_non_null", "reference/api/_autosummary/cyclops.data.slicer.filter_range", "reference/api/_autosummary/cyclops.data.slicer.filter_string_contains", "reference/api/_autosummary/cyclops.data.slicer.filter_value", "reference/api/_autosummary/cyclops.data.slicer.is_datetime", "reference/api/_autosummary/cyclops.data.slicer.overall", "reference/api/_autosummary/cyclops.monitor.clinical_applicator", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift", "reference/api/_autosummary/cyclops.report.report", "reference/api/_autosummary/cyclops.report.report.ModelCardReport", "reference/api/_autosummary/cyclops.tasks.classification", "reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask", "reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask", "reference/api/cyclops.data", "reference/api/cyclops.monitor", "reference/api/cyclops.report", "reference/api/cyclops.tasks", "reference/api/evaluator", "reference/api/fairness_evaluator", "reference/api/metrics", "reference/api/metrics/accuracy", "reference/api/metrics/auroc", "reference/api/metrics/average_precision", "reference/api/metrics/f1score", "reference/api/metrics/f_beta", "reference/api/metrics/precision", "reference/api/metrics/precision_recall_curve", "reference/api/metrics/recall", "reference/api/metrics/roc", "reference/api/metrics/sensitivity", "reference/api/metrics/specificity", "reference/api/metrics/stat_scores", "tutorials", "tutorials/diabetes_130/readmission_prediction", "tutorials/kaggle/heart_failure_prediction", "tutorials/mimiciv/mortality_prediction", "tutorials/nihcxr/cxr_classification", "tutorials/nihcxr/monitor_api", "tutorials/synthea/los_prediction", "tutorials_monitor", "tutorials_use_cases", "user_guide"], "filenames": ["api.rst", "contributing.rst", "developing.rst", "evaluation.rst", "examples/metrics.ipynb", "examples/report.ipynb", "index.rst", "installation.rst", "model_report.rst", "monitoring.rst", "reference/api/_autosummary/cyclops.data.aggregate.rst", "reference/api/_autosummary/cyclops.data.aggregate.Aggregator.rst", "reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.rst", "reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.rst", "reference/api/_autosummary/cyclops.data.features.medical_image.rst", "reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.rst", "reference/api/_autosummary/cyclops.data.slicer.rst", "reference/api/_autosummary/cyclops.data.slicer.SliceSpec.rst", "reference/api/_autosummary/cyclops.data.slicer.compound_filter.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_datetime.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_non_null.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_range.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_value.rst", "reference/api/_autosummary/cyclops.data.slicer.is_datetime.rst", "reference/api/_autosummary/cyclops.data.slicer.overall.rst", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.rst", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.rst", "reference/api/_autosummary/cyclops.report.report.rst", "reference/api/_autosummary/cyclops.report.report.ModelCardReport.rst", "reference/api/_autosummary/cyclops.tasks.classification.rst", "reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.rst", "reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.rst", "reference/api/cyclops.data.rst", "reference/api/cyclops.monitor.rst", "reference/api/cyclops.report.rst", "reference/api/cyclops.tasks.rst", "reference/api/evaluator.rst", "reference/api/fairness_evaluator.rst", "reference/api/metrics.rst", "reference/api/metrics/accuracy.rst", "reference/api/metrics/auroc.rst", "reference/api/metrics/average_precision.rst", "reference/api/metrics/f1score.rst", "reference/api/metrics/f_beta.rst", "reference/api/metrics/precision.rst", "reference/api/metrics/precision_recall_curve.rst", "reference/api/metrics/recall.rst", "reference/api/metrics/roc.rst", "reference/api/metrics/sensitivity.rst", "reference/api/metrics/specificity.rst", "reference/api/metrics/stat_scores.rst", "tutorials.rst", "tutorials/diabetes_130/readmission_prediction.ipynb", "tutorials/kaggle/heart_failure_prediction.ipynb", "tutorials/mimiciv/mortality_prediction.ipynb", "tutorials/nihcxr/cxr_classification.ipynb", "tutorials/nihcxr/monitor_api.ipynb", "tutorials/synthea/los_prediction.ipynb", "tutorials_monitor.rst", "tutorials_use_cases.rst", "user_guide.rst"], "titles": ["API Reference", "Contributing to cyclops", "\ud83e\uddd1\ud83c\udfff\u200d\ud83d\udcbb Developing", "Evaluation", "Breast Cancer Classification and Evaluation", "Report Generation for Heart Failure Prediction", "What is CyclOps?", "Installation", "Model Report", "Monitoring", "cyclops.data.aggregate", "cyclops.data.aggregate.Aggregator", "cyclops.data.aggregate.tabular_as_aggregated", "cyclops.data.aggregate.timestamp_ffill_agg", "cyclops.data.features.medical_image", "cyclops.data.features.medical_image.MedicalImage", "cyclops.data.slicer", "cyclops.data.slicer.SliceSpec", "cyclops.data.slicer.compound_filter", "cyclops.data.slicer.filter_datetime", "cyclops.data.slicer.filter_non_null", "cyclops.data.slicer.filter_range", "cyclops.data.slicer.filter_string_contains", "cyclops.data.slicer.filter_value", "cyclops.data.slicer.is_datetime", "cyclops.data.slicer.overall", "cyclops.monitor.clinical_applicator", "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator", "cyclops.monitor.synthetic_applicator", "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator", "cyclops.monitor.synthetic_applicator.binary_noise_shift", "cyclops.monitor.synthetic_applicator.feature_association_shift", "cyclops.monitor.synthetic_applicator.feature_swap_shift", "cyclops.monitor.synthetic_applicator.gaussian_noise_shift", "cyclops.monitor.synthetic_applicator.knockout_shift", "cyclops.report.report", "cyclops.report.report.ModelCardReport", "cyclops.tasks.classification", "cyclops.tasks.classification.BinaryTabularClassificationTask", "cyclops.tasks.classification.MultilabelImageClassificationTask", "cyclops.data", "cyclops.monitor", "cyclops.report", "cyclops.tasks", "Evaluator", "Fairness Evaluator", "Evaluation Metrics", "Accuracy", "AUROC", "Average Precision", "F1-Score", "F-beta Score", "Precision", "PrecisionRecallCurve", "Recall", "ROC Curve", "Sensitivity", "Specificity", "StatScores", "Tutorials", "Readmission Prediction", "Heart Failure Prediction", "Mortality Prediction", "Chest X-Ray Disease Classification", "NIHCXR Clinical Drift Experiments Tutorial", "Prolonged Length of Stay Prediction", "monitor API", "Example use cases", "User Guide"], "terms": {"cyclop": [0, 2, 4, 5, 7, 8, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65], "data": [0, 3, 6, 8, 9, 27, 29, 30, 31, 32, 33, 34, 36, 38, 39, 47, 50, 51, 53, 55, 59, 63, 64, 66, 68], "task": [0, 6, 44, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 67], "evalu": [0, 6, 8, 9, 36, 38, 39, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 63, 67, 68], "fair": [0, 3, 6, 36, 38, 39, 44, 60, 61, 62, 63, 65, 68], "metric": [0, 3, 6, 8, 9, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65, 67, 68], "monitor": [0, 5, 6, 8, 60, 61, 62, 64, 65, 68], "report": [0, 4, 6, 9, 58, 67, 68], "thank": 1, "your": [1, 3, 4, 5, 8, 61], "interest": [1, 4, 60, 61, 62, 65], "To": [1, 5, 9, 15, 61, 65], "submit": 1, "pr": 1, "pleas": 1, "fill": [1, 5, 13], "out": [1, 5, 36, 60, 61, 62, 65], "templat": [1, 36], "along": [1, 5, 27, 60, 61, 62, 63, 65], "If": [1, 2, 11, 13, 15, 17, 19, 20, 21, 22, 23, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65], "fix": 1, "an": [1, 4, 5, 6, 8, 9, 11, 15, 17, 36, 44, 45, 50, 52, 54, 56, 58, 60, 61, 62, 63, 65, 67], "issu": [1, 45], "don": [1, 62], "t": [1, 4, 5, 8, 15, 36, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 62], "forget": 1, "link": [1, 5, 8, 36, 60, 61, 62, 63, 65], "onc": [1, 5, 9, 60, 61, 62, 65], "python": [1, 60, 62, 65], "virtual": 1, "environ": [1, 2], "i": [1, 2, 4, 5, 7, 8, 9, 11, 12, 13, 15, 17, 19, 20, 21, 22, 23, 24, 27, 29, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65, 67], "setup": 1, "you": [1, 2, 3, 4, 5, 8, 9, 15, 60, 61, 62, 63, 65, 66], "can": [1, 2, 4, 5, 7, 8, 9, 13, 15, 17, 36, 38, 39, 45, 47, 50, 52, 54, 55, 56, 58, 60, 61, 62, 63, 65, 66], "run": [1, 2, 5, 61, 62, 65], "us": [1, 3, 5, 6, 8, 9, 11, 15, 17, 18, 27, 29, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66, 68], "all": [1, 4, 5, 8, 12, 13, 17, 18, 19, 20, 21, 22, 23, 25, 36, 38, 54, 56, 58, 60, 61, 62, 63, 64, 65], "file": [1, 5, 9, 15, 36, 60, 61, 62, 63, 65], "For": [1, 4, 5, 7, 36, 45, 58, 60, 61, 62, 65], "style": 1, "we": [1, 4, 5, 9, 36, 60, 61, 62, 63, 65], "recommend": [1, 4], "pep": 1, "8": [1, 17, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65], "guid": [1, 6, 8], "docstr": 1, "numpi": [1, 4, 5, 13, 30, 31, 32, 33, 34, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65], "format": [1, 5, 11, 15, 17, 18, 19, 20, 21, 22, 23, 25, 36, 38, 39, 44, 45, 53, 58, 60, 61, 62, 65], "ruff": [1, 60, 61, 62], "static": [1, 12], "analysi": [1, 5, 36, 60, 61, 62, 63, 65, 68], "check": [1, 8, 24, 53], "variou": [1, 8, 44, 60, 61, 62, 65], "rule": 1, "includ": [1, 5, 11, 17, 21, 27, 29, 36, 45, 60, 61, 62, 63, 65, 66, 67], "flake8": 1, "The": [1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 15, 17, 18, 19, 20, 21, 22, 23, 24, 27, 29, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65, 66, 67], "show": [1, 4, 5, 6, 60, 61, 62, 63, 65], "error": [1, 44, 45], "which": [1, 5, 11, 13, 19, 20, 21, 22, 23, 36, 44, 45, 53, 60, 61, 62, 63, 65, 67], "need": [1, 2, 4, 5, 8, 44, 45, 60, 61, 62, 65], "befor": [1, 5, 13, 38, 44, 45, 60, 61, 62, 65], "last": [1, 8, 12], "least": 1, "type": [1, 4, 5, 11, 12, 13, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 67], "hint": 1, "our": [1, 4, 5, 60, 61, 62, 65], "mypi": 1, "set": [2, 7, 9, 11, 12, 17, 36, 44, 45, 47, 49, 50, 51, 52, 54, 56, 57, 58, 60, 61, 62, 63, 65], "up": [2, 4, 5, 60, 61, 62, 65], "henc": [2, 7], "make": [2, 4, 5, 60, 61, 62, 65], "sure": [2, 5, 61], "instal": [2, 5, 6, 61, 67, 68], "python3": [2, 7], "m": [2, 7, 61, 62, 63, 64, 65], "sourc": [2, 6, 11, 12, 13, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 66], "env": 2, "info": 2, "path": [2, 5, 15, 27, 36, 38, 39, 60, 61, 62, 65], "bin": [2, 11, 45], "activ": [2, 65], "In": [2, 4, 5, 8, 60, 61, 62, 65], "order": [2, 15, 44, 58], "depend": [2, 7, 8], "test": [2, 4, 8, 9, 29, 36, 38, 39, 44, 60, 61, 62, 65, 66, 67, 68], "codestyl": 2, "unit": 2, "integr": 2, "api": [2, 3, 5, 6, 11, 44, 45, 61, 65, 67], "document": [2, 5, 8, 60, 61, 62, 63, 65], "built": 2, "sphinx": 2, "local": 2, "doc": 2, "cd": 2, "html": [2, 5, 36, 60, 61, 62, 63, 65], "sphinxopt": 2, "d": [2, 5, 27, 61, 63], "nbsphinx_allow_error": 2, "true": [2, 4, 5, 11, 12, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 31, 36, 38, 39, 44, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65], "build": [2, 27], "pandoc": 2, "addit": [2, 4, 5, 7, 36, 38, 39, 60, 61, 62, 63, 65], "group": [2, 4, 5, 8, 11, 36, 45, 60, 61, 62, 65], "welcom": 2, "see": [2, 4, 5, 8, 9, 11, 17, 36, 60, 61, 62, 65], "guidelin": [2, 6], "equip": 3, "rich": 3, "toolbox": 3, "assess": [3, 36], "model": [3, 4, 6, 7, 9, 35, 36, 38, 39, 44, 45, 66, 67, 68], "across": [3, 8, 31, 45, 66], "dimens": 3, "dive": [3, 8], "detail": [3, 5, 17, 60, 61, 62, 63, 65, 68], "perform": [3, 4, 5, 6, 8, 11, 13, 23, 36, 44, 47, 48, 60, 61, 62, 65, 66, 67, 68], "unveil": 3, "potenti": 3, "concern": 3, "gain": [3, 8], "granular": 3, "insight": [3, 8], "through": [3, 5, 13, 60, 61, 62, 63, 65], "slice": [3, 8, 17, 18, 36, 38, 39, 44, 45, 60, 61, 62, 63, 65, 68], "emploi": 3, "robust": [3, 6], "select": [3, 5, 8, 9, 17, 27, 31, 60, 61, 62, 63, 65], "common": [3, 5, 61, 62], "": [3, 4, 5, 6, 8, 9, 17, 20, 22, 24, 36, 38, 39, 44, 45, 49, 60, 61, 62, 65], "effect": 3, "identifi": [3, 5, 36, 63, 67], "area": [3, 5, 48, 60, 61, 62, 65], "improv": [3, 5, 6, 60, 61, 62, 65], "isol": 3, "behavior": 3, "specif": [3, 5, 6, 7, 8, 17, 38, 39, 44, 46, 49, 60, 61, 62, 63, 65], "subset": [3, 4, 5, 16, 60], "reveal": 3, "nuanc": 3, "demograph": [3, 5, 60, 61, 63], "featur": [3, 5, 8, 9, 17, 20, 23, 27, 31, 32, 36, 38, 63, 67, 68], "other": [3, 4, 5, 60, 61], "import": [3, 4, 5, 17, 27, 29, 36, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 66, 67], "characterist": [3, 5, 60, 61, 62, 65], "uncov": 3, "analyz": 3, "bias": [3, 5, 8, 60, 61, 62, 63, 65], "within": [3, 31, 60, 62], "ensur": [3, 8, 63], "respons": 3, "equit": 3, "outcom": [3, 8, 67, 68], "follow": [3, 5, 8, 11, 17, 36, 44, 47, 50, 51, 52, 53, 54, 56, 57, 60, 61, 62, 63, 65], "exampl": [3, 4, 5, 6, 7, 8, 11, 15, 17, 18, 19, 20, 21, 22, 23, 25, 27, 29, 36, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 66], "below": [3, 5, 7, 8, 9, 60, 61, 62, 65], "instruct": [3, 8, 65], "breast": [3, 68], "cancer": [3, 68], "classif": [3, 5, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 65, 68], "individu": [3, 5, 31, 60, 61, 62, 65, 68], "metricdict": [3, 5, 38, 39, 44, 45, 60, 61, 62, 65, 68], "intersect": [3, 6, 17, 45, 68], "prepar": [3, 68], "result": [3, 8, 12, 18, 38, 39, 44, 45, 50, 60, 61, 62, 64, 65, 68], "dataset": [4, 7, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 29, 36, 38, 39, 40, 44, 45, 47, 50, 51, 52, 53, 54, 55, 56, 57, 66, 67, 68], "well": [4, 5, 60, 61, 62, 63, 65], "suit": [4, 8], "demonstr": [4, 5, 67], "due": [4, 65], "its": [4, 5, 6, 8, 17, 52, 54, 56, 60, 61, 62, 63, 65], "two": 4, "distinct": [4, 8], "class": [4, 5, 10, 11, 14, 15, 16, 17, 26, 27, 28, 29, 31, 32, 34, 35, 36, 37, 38, 39, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65], "binari": [4, 5, 15, 30, 38, 39, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65, 67], "complet": 4, "absenc": [4, 52, 54, 56], "miss": 4, "valu": [4, 5, 9, 11, 12, 13, 15, 17, 18, 19, 20, 21, 22, 23, 24, 27, 36, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 62, 63, 64, 65, 67], "thi": [4, 5, 8, 9, 12, 17, 36, 38, 44, 45, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 60, 61, 62, 63, 65, 67], "clean": [4, 62], "organ": [4, 5, 60, 61, 62, 65], "structur": [4, 60, 61, 62, 65], "ideal": [4, 8], "start": [4, 5, 11, 60, 61, 62, 65], "point": [4, 20, 58], "explor": [4, 5, 8], "np": [4, 5, 15, 21, 24, 38, 39, 45, 60, 61, 62, 63, 64, 65], "panda": [4, 5, 11, 12, 13, 60, 61, 62, 65], "pd": [4, 5, 11, 12, 38, 60, 61, 62], "from": [4, 5, 8, 11, 15, 17, 27, 29, 34, 36, 38, 39, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 67], "arrow_dataset": 4, "sklearn": [4, 5, 11, 29, 60, 61, 62, 65], "model_select": [4, 5], "train_test_split": [4, 5, 29, 60, 61, 62, 65], "svm": 4, "svc": 4, "slicer": [4, 5, 60, 61, 62, 63, 64, 65], "slicespec": [4, 5, 27, 39, 44, 60, 61, 62, 63, 64, 65], "evaluate_fair": [4, 45], "binaryaccuraci": [4, 5, 60, 61, 62, 65], "create_metr": [4, 5, 60, 61, 62, 63, 65], "experiment": [4, 5, 60, 61, 62, 63, 65], "binaryauroc": [4, 5, 60, 61, 62, 65], "binaryaverageprecis": [4, 5, 60, 61, 62], "metric_dict": [4, 5, 60, 61, 62, 65], "plot": [4, 5, 8, 9, 36, 60, 61, 62, 63, 65], "classificationplott": [4, 5, 60, 61, 62, 65], "load": [4, 7, 8, 36, 38, 39, 44, 65, 66, 67, 68], "breast_cancer_data": 4, "load_breast_canc": 4, "as_fram": 4, "x": [4, 5, 8, 9, 29, 30, 31, 32, 33, 34, 38, 59, 60, 61, 62, 64, 65], "y": [4, 5, 11, 29, 31, 32, 34, 38, 62, 63, 65], "target": [4, 5, 27, 31, 32, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65, 66, 67], "just": [4, 60], "take": [4, 5, 27, 45, 47, 48, 49, 50, 51, 52, 54, 56, 57, 60, 61], "quick": [4, 8, 9, 60, 61, 62, 63, 65], "look": 4, "stat": [4, 58], "df": [4, 5, 60, 61, 62, 65], "frame": 4, "describ": [4, 5], "split": [4, 5, 27, 36, 38, 39, 44, 60, 61, 62, 63, 65], "train": [4, 8, 36, 38, 39, 44, 63, 66, 67, 68], "x_train": [4, 5, 38], "x_test": [4, 5], "y_train": [4, 5, 38], "y_test": [4, 5], "test_siz": [4, 5, 29], "0": [4, 5, 13, 17, 29, 30, 31, 32, 33, 34, 36, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65], "1": [4, 5, 11, 13, 17, 27, 31, 32, 33, 34, 36, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65, 66, 67], "random_st": [4, 5, 60, 61, 62, 65], "13": [4, 58], "classifi": [4, 5, 47, 50, 51, 52, 54, 56, 57, 60, 61, 62, 65], "c": 4, "10": [4, 5, 58, 60, 61, 62, 64, 65], "gamma": [4, 60, 62, 65], "01": [4, 17, 48, 60, 61, 62, 65], "probabl": [4, 38, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 65], "fit": [4, 5, 11, 38, 60, 61, 62, 65], "predict": [4, 6, 8, 38, 39, 44, 45, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 63, 68], "y_pred": [4, 5, 60, 61, 62, 65], "y_pred_prob": [4, 5], "predict_proba": [4, 5], "now": [4, 5], "either": [4, 11, 21, 36, 58, 60, 61, 62, 65], "each": [4, 5, 8, 9, 11, 17, 18, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65], "call": [4, 15, 36], "them": [4, 5, 8, 47, 57, 60, 61, 62, 65, 66], "defin": [4, 5, 36, 60, 61, 62, 63, 65], "object": [4, 5, 17, 27, 29, 36, 38, 39, 45, 60, 61, 62, 65], "here": [4, 5, 8, 17, 60, 61, 62, 65], "both": 4, "method": [4, 5, 11, 15, 17, 27, 29, 36, 38, 39, 44, 45, 48, 60, 61, 62, 65], "case": [4, 5, 6, 8, 30, 36, 59, 60, 61, 62, 65, 66], "onli": [4, 44, 45, 47, 48, 50, 51, 52, 54, 56, 57, 58], "singl": [4, 8, 9, 11, 13, 17, 36, 38, 39, 44, 53, 60, 61, 62, 65, 67], "creat": [4, 7, 8, 16, 17, 30, 33, 34, 36, 38, 39, 45, 52, 54, 56, 63, 67, 68], "desir": 4, "ground": [4, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58], "truth": [4, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58], "bin_acc_metr": 4, "float64": [4, 5, 30, 31, 32, 33, 34], "mai": [4, 5, 8, 44, 45, 60, 61, 62, 63, 65], "collect": [4, 67], "more": [4, 5, 17, 38, 39, 44, 60, 61, 67], "It": [4, 44, 50, 52, 54, 56, 57, 66], "also": [4, 5, 8, 47, 49, 50, 51, 52, 54, 56, 57, 60, 61, 62, 63, 65, 67], "speed": 4, "calcul": [4, 36, 47, 48, 49, 50, 51, 52, 54, 56, 57, 60, 61], "metric_nam": [4, 5, 36, 45, 60, 61, 62, 63, 65], "binary_accuraci": [4, 5, 60, 61, 62, 65], "binary_precis": [4, 5, 60, 61, 62, 65], "binary_recal": [4, 5, 54, 60, 61, 62, 65], "binary_f1_scor": [4, 5, 60, 61, 62, 65], "binary_roc_curv": [4, 5, 60, 61, 62, 65], "metric_collect": [4, 5, 60, 61, 62, 65], "reset": 4, "add": [4, 5, 8, 17, 36, 38, 39, 60, 61, 62, 65], "add_metr": 4, "overal": [4, 5, 9, 17, 36, 45, 60, 61, 62, 63, 65], "might": [4, 5, 60, 61, 62, 65], "how": [4, 5, 6, 8, 9, 11, 60, 61, 62, 63, 65], "certain": [4, 5, 60, 61, 62, 65], "subpopul": [4, 5, 60, 61, 62, 63, 65], "spec_list": [4, 5, 17, 60, 61, 62, 63, 64, 65], "worst": 4, "radiu": 4, "min_valu": [4, 5, 17, 21, 61, 62, 63, 64, 65], "14": [4, 62, 63, 67], "max_valu": [4, 5, 17, 21, 61, 62, 63, 64, 65], "15": [4, 57, 58], "min_inclus": [4, 5, 17, 21, 61, 62, 65], "max_inclus": [4, 5, 17, 21, 61, 62, 65], "fals": [4, 5, 15, 17, 19, 20, 21, 22, 23, 24, 32, 33, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65], "17": [4, 58], "textur": 4, "23": [4, 17], "28": 4, "7": [4, 5, 17, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65, 67], "slice_spec": [4, 5, 17, 38, 39, 44, 60, 61, 62, 63, 65], "when": [4, 5, 9, 15, 38, 39, 44, 45, 47, 49, 50, 51, 52, 54, 56, 57, 58, 60, 61, 62, 65], "ar": [4, 5, 6, 7, 8, 9, 15, 17, 21, 22, 27, 31, 36, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65], "specifi": [4, 5, 17, 27, 30, 33, 36, 38, 39, 44, 60, 61, 62, 65], "sometim": 4, "wish": 4, "combin": [4, 5, 17, 18, 61], "argument": [4, 5, 17, 38, 39, 44, 45, 60, 61, 62, 65], "2": [4, 5, 13, 17, 31, 32, 36, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65, 66], "huggingfac": [4, 8, 27, 38, 39, 45, 68], "so": [4, 5, 9], "datafram": [4, 5, 11, 12, 38, 60, 61, 62, 65], "concat": [4, 62], "column": [4, 5, 11, 12, 17, 18, 19, 20, 21, 22, 23, 27, 38, 39, 44, 45, 60, 61, 62, 63, 65], "axi": [4, 8, 9, 32, 62, 65], "pred": [4, 5, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65], "preds_prob": [4, 5], "from_panda": [4, 5, 60, 61, 62, 65], "breast_cancer_sliced_result": 4, "ignor": [4, 5, 44, 60, 61, 62, 65], "list": [4, 5, 7, 11, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 30, 31, 32, 33, 34, 36, 38, 39, 44, 45, 48, 49, 53, 55, 60, 61, 62, 65], "item": [4, 5, 60, 61, 62, 63, 64, 65], "target_column": [4, 5, 44, 45, 60, 61, 62, 63, 65], "prediction_column": [4, 5, 44, 45, 63], "visual": [4, 8, 9], "binaryf1scor": [4, 5, 60, 61, 62, 65], "binaryprecis": [4, 5, 60, 61, 62, 65], "differ": [4, 5, 8, 9, 17, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 65, 66], "extract": [4, 5, 60, 61, 62, 65], "slice_metr": [4, 5, 60, 61, 62, 65], "slice_nam": [4, 5, 17, 36, 60, 61, 62, 65], "metric_valu": [4, 5, 60, 61, 62, 65], "slice_result": [4, 5, 60, 61, 62, 65], "model_for_preds_prob": [4, 5], "plotter": [4, 5, 60, 61, 62, 64, 65], "task_typ": [4, 5, 38, 39, 60, 61, 62, 65], "class_nam": [4, 5, 60, 61, 62, 65], "set_templ": [4, 5, 60, 61, 62, 65], "plotly_whit": [4, 5, 60, 61, 62, 65], "slice_metrics_plot": [4, 5, 60, 61, 62, 65], "metrics_comparison_bar": [4, 5, 60, 61, 62, 65], "veri": [4, 45], "good": [4, 9], "appli": [4, 5, 18, 27, 38, 39, 47, 48, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65], "mean": [4, 5, 11, 36, 47, 48, 49, 50, 51, 52, 54, 56, 57, 60, 61, 62, 63, 65], "discret": 4, "optim": [4, 45, 61], "should": [4, 5, 8, 11, 17, 18, 32, 36, 38, 39, 44, 45, 55, 60, 61, 62, 63, 65], "have": [4, 5, 8, 9, 19, 22, 23, 29, 60, 61, 62, 67], "less": [4, 17, 21, 51, 60, 62, 65], "than": [4, 5, 17, 21, 38, 39, 51, 60, 61, 62, 65, 67], "50": [4, 5, 60, 61, 62, 64, 65], "uniqu": [4, 45, 48, 49, 53, 55, 60, 63, 67], "categori": [4, 62, 65], "fairness_result": [4, 60, 61, 62, 65], "copi": [5, 60, 61, 62, 63, 65], "inspect": [5, 60, 61, 67], "o": 5, "shutil": [5, 61, 62, 63, 65], "datetim": [5, 17, 19, 21, 24, 36, 60, 61, 62, 65], "date": [5, 36, 60, 61, 62, 65], "plotli": [5, 36, 60, 61, 62, 63, 65], "express": [5, 60, 61, 62, 63, 65], "px": [5, 60, 61, 62, 63, 65], "kaggl": [5, 59, 61], "kaggle_api_extend": [5, 61], "kaggleapi": [5, 61], "linear_model": [5, 61], "logisticregress": 5, "minmaxscal": [5, 60, 61, 62, 65], "tqdm": 5, "modelcardreport": [5, 60, 61, 62, 63, 65], "util": [5, 60, 61, 62, 63, 65], "flatten_results_dict": [5, 60, 61, 62, 65], "proce": 5, "step": [5, 60, 61, 62, 65], "purpos": 5, "go": [5, 60, 61, 62, 63, 65], "gradual": 5, "popul": [5, 36, 60, 61, 62, 65, 67], "inform": [5, 8, 36, 60, 61], "about": [5, 8, 9, 36, 60, 61, 62, 63, 65], "first": [5, 12, 44, 45, 47, 57, 60, 61, 62, 65], "field": [5, 36, 60, 61, 62, 65, 67], "section": [5, 8, 36, 60, 61, 62, 63, 65], "after": [5, 9, 60, 61, 62, 65], "constant": [5, 67], "data_dir": [5, 61, 63], "random_se": [5, 60, 61, 62, 65], "21": [5, 65], "pip": [5, 60, 61, 68], "sign": [5, 61, 65], "account": [5, 6, 47, 48, 49, 50, 51, 52, 54, 56, 57, 61, 63], "http": [5, 36, 60, 61, 62, 63, 65], "www": [5, 61], "com": [5, 61, 63], "Then": [5, 61], "tab": [5, 12, 61], "user": [5, 6, 8, 9, 36, 60, 61, 62, 65], "profil": [5, 61], "token": [5, 15, 61], "trigger": [5, 61], "download": [5, 61], "json": [5, 9, 36, 60, 61, 62, 65], "contain": [5, 11, 12, 15, 17, 18, 19, 20, 21, 22, 23, 25, 36, 44, 45, 47, 53, 55, 57, 60, 61, 62, 63, 65, 67], "credenti": [5, 61], "place": [5, 61], "locat": [5, 61], "machin": [5, 61, 63, 67], "authent": [5, 61], "dataset_download_fil": [5, 61], "fedesoriano": [5, 61], "unzip": [5, 61], "read_csv": 5, "join": [5, 61, 62, 65], "csv": [5, 61, 65], "head": 5, "fig": [5, 36, 60, 61, 62, 63, 65], "pie": [5, 60, 61, 62, 63, 65], "name": [5, 11, 12, 17, 18, 19, 20, 21, 22, 23, 27, 36, 38, 39, 44, 45, 53, 55, 60, 61, 62, 63, 64, 65], "sex": [5, 9, 27, 62, 64, 65, 67], "update_layout": [5, 60, 61, 62, 63, 65], "titl": [5, 8, 60, 61, 62, 63, 65], "diagram": 5, "caption": [5, 36, 60, 61, 62, 63, 65], "belong": 5, "sinc": 5, "log_plotly_figur": [5, 36, 60, 61, 62, 63, 65], "section_nam": [5, 36, 60, 61, 62, 63, 65], "histogram": [5, 60, 61, 62, 63, 65], "similarli": 5, "appear": [5, 9], "under": [5, 48, 60, 61, 62, 65], "xaxis_titl": [5, 60, 61, 62, 63, 65], "yaxis_titl": [5, 60, 61, 62, 63, 65], "count": [5, 11, 47, 48, 49, 50, 51, 52, 54, 56, 57, 60, 61, 62, 63, 65], "bargap": [5, 60, 61, 62, 63, 65], "heartdiseas": [5, 61], "astyp": [5, 60, 61, 65], "int": [5, 11, 12, 13, 17, 19, 27, 31, 32, 33, 34, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65], "drop": [5, 9, 60, 61, 67], "update_trac": [5, 60, 61, 62, 63, 65], "textinfo": [5, 60, 61, 62, 65], "percent": [5, 60, 61, 62, 65], "label": [5, 31, 32, 34, 38, 39, 44, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 63, 67], "title_text": [5, 60, 61, 62, 65], "hovertempl": [5, 60, 61, 62, 65], "br": [5, 60, 61, 62, 65], "class_count": [5, 60, 61, 62, 65], "value_count": [5, 60, 61, 62, 65], "class_ratio": [5, 60, 61, 62, 65], "print": [5, 17, 60, 61, 62, 63, 65], "doe": [5, 17, 19, 21, 22, 23, 36, 45, 47, 48, 49, 50, 51, 52, 54, 56, 57], "ani": [5, 8, 13, 17, 19, 22, 23, 36, 38, 39, 44, 45, 60, 61, 62, 63, 65], "null": [5, 17, 19, 20, 21, 22, 23, 62, 65], "jump": 5, "scale": 5, "string": [5, 11, 15, 17, 19, 22, 36, 44, 45, 60], "form": [5, 13, 50, 60, 61, 62, 65], "convert": [5, 15, 47, 49, 51, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65], "back": [5, 36], "work": [5, 6, 17, 36, 47, 57, 60, 61, 62, 63, 65], "string_col": 5, "select_dtyp": 5, "to_list": 5, "tree": 5, "base": [5, 9, 11, 15, 17, 27, 29, 36, 38, 39, 44, 45, 52, 54, 56, 60, 61, 67], "labelencod": 5, "df_process": 5, "get_dummi": 5, "drop_first": 5, "feature_col": 5, "remov": [5, 34, 38, 39, 45, 60, 61, 62, 63, 65], "let": [5, 8, 60, 61, 62, 65], "done": [5, 11, 60, 61, 62, 65], "log_dataset": [5, 36, 60, 61], "descript": [5, 8, 36, 60, 61, 62, 63, 65], "A": [5, 13, 17, 18, 19, 20, 21, 22, 23, 25, 36, 44, 45, 47, 53, 55, 57, 58, 60, 61, 62, 63, 65], "citat": [5, 36, 60, 61, 62, 63, 65], "resourc": [5, 36, 60, 61, 62, 65], "license_id": [5, 36, 60, 61], "spdx": [5, 36, 60, 61], "licens": [5, 36, 60, 61, 62, 63, 65], "version": [5, 11, 15, 36, 60, 61, 62, 63, 65], "valid": [5, 17, 19, 36, 38, 39, 44, 60, 61, 62], "etc": [5, 8, 31, 32, 60, 61, 62, 63, 65], "sensitive_featur": [5, 36, 60, 61], "sensit": [5, 8, 36, 46, 60, 61, 62, 63, 65, 66], "sensitive_feature_justif": [5, 36, 60, 61], "justif": [5, 36, 60, 61], "wa": [5, 9, 11, 60, 61, 62, 63, 65], "alreadi": [5, 60, 61, 62, 65], "avail": [5, 36, 44, 60, 61, 62, 65, 67], "independ": [5, 8, 61], "5": [5, 17, 29, 30, 33, 34, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65, 66], "over": [5, 6, 8, 9, 36, 58, 60, 61, 62, 63, 65], "11": [5, 52, 61, 62, 63, 65, 67], "everi": [5, 61], "found": [5, 36, 61], "index": [5, 12, 60, 61, 62, 65], "diseas": [5, 61, 67], "uci": [5, 61, 67], "learn": [5, 61, 63, 67], "repositori": [5, 8, 15, 61, 67], "archiv": [5, 61], "ic": [5, 61], "edu": [5, 61], "ml": [5, 60, 61, 62, 65], "databas": [5, 60, 61, 62, 65], "cleandoc": [5, 60, 61], "misc": [5, 61], "author": [5, 60, 61, 63], "f": [5, 46, 50, 60, 61, 62, 63, 64, 65], "year": [5, 17, 19, 59, 60, 61, 62, 63, 65], "2021": [5, 17, 61, 62, 65], "publish": [5, 60, 61], "cc0": [5, 60, 61], "like": [5, 8, 47, 52, 54, 56, 60, 61], "gender": [5, 8, 60, 61, 63, 64, 67], "often": [5, 60, 61], "strong": [5, 60, 61], "correl": [5, 60, 61], "health": [5, 6, 60, 61, 62, 65], "older": [5, 60, 61, 62, 65], "patient": [5, 60, 61, 62, 63, 64, 65, 67], "higher": [5, 60, 61], "risk": [5, 8, 36, 60, 61, 62, 63, 65, 67], "process": [5, 12, 27, 60, 61, 63, 65, 67], "hug": [5, 16, 38, 39, 67], "face": [5, 16, 38, 39, 67], "later": 5, "cleanup_cache_fil": [5, 60, 61, 62, 65], "input": [5, 11, 15, 30, 33, 38, 39, 47, 49, 52, 53, 54, 55, 56, 58, 60], "output": [5, 11, 36, 47, 53, 55, 60, 61, 62, 65], "scaler": [5, 60, 61, 62, 65], "fit_transform": [5, 11, 62], "As": [5, 36], "tutori": [5, 6, 66, 67], "dict": [5, 11, 15, 17, 36, 38, 39, 44, 45], "binary_auroc": [5, 60, 61, 62, 65], "30": [5, 60, 61, 63, 67], "70": [5, 60, 61], "raw": 5, "get": [5, 9, 38, 39, 60, 61, 62, 65], "posit": [5, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65], "match": [5, 17, 19, 22, 23, 44], "indic": [5, 9, 17, 30, 33, 44, 45, 47, 52, 54, 56, 60, 61, 62, 65], "matching_posit": 5, "get_index": 5, "row": [5, 17, 63], "non": [5, 12, 53, 55], "neg": [5, 13, 44, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 63, 65], "df_test": 5, "iloc": 5, "gather": 5, "evalutaion": 5, "log_performance_metr": [5, 36, 60, 61, 62, 65], "expect": [5, 52, 54, 56, 60, 61, 62, 65], "dictionari": [5, 11, 15, 17, 18, 36, 38, 39, 44, 45, 60, 61, 62, 65], "where": [5, 6, 8, 17, 18, 19, 20, 21, 22, 23, 36, 38, 39, 52, 53, 54, 56, 60, 61, 62, 65, 67], "kei": [5, 8, 17, 36, 45, 60, 61, 62, 63, 64, 65, 68], "instanc": [5, 11, 47, 48, 49, 50, 51, 52, 54, 56, 57, 60, 61, 62, 63, 65], "accuraci": [5, 8, 46, 60, 61, 62, 65], "right": [5, 9, 60, 61, 62, 65], "heart_failure_data": 5, "results_flat": [5, 60, 61, 62, 63, 65], "remove_metr": [5, 60, 61, 62, 65], "binaryroc": [5, 60, 61, 62, 65], "model_nam": [5, 38, 39, 60, 61, 62, 63, 64, 65], "hover": [5, 8], "feel": 5, "free": 5, "chang": [5, 8, 36, 60, 61, 62, 63, 65], "appropri": [5, 52, 54, 56, 60, 61, 62, 65], "usag": [5, 45, 60, 61, 62, 65], "noqa": [5, 60, 61, 62, 63, 65], "plw2901": [5, 60, 61, 62, 63, 65], "proport": [5, 30, 60, 61, 62, 63, 65], "correctli": [5, 52, 60, 61, 62, 63, 65], "binaryrecal": [5, 60, 61, 62, 65], "actual": [5, 60, 61, 62, 63, 65], "known": [5, 60, 61, 62, 63, 65], "recal": [5, 46, 47, 49, 50, 53, 55, 56, 57, 60, 61, 62, 63, 65], "rate": [5, 48, 55, 60, 61, 62, 63, 65], "receiv": [5, 60, 61, 62, 65], "oper": [5, 45, 60, 61, 62, 65], "curv": [5, 46, 48, 53, 60, 61, 62, 65], "auroc": [5, 46, 60, 61, 62, 65, 67], "measur": [5, 60, 61, 62, 65], "precis": [5, 46, 47, 50, 51, 53, 54, 55, 56, 57, 60, 61, 62, 63, 65], "auprc": [5, 60, 61, 62], "harmon": [5, 51, 60, 61, 62, 65], "log_quantitative_analysi": [5, 36, 60, 61, 62, 63, 65], "tolist": [5, 53, 60, 61, 62, 63, 65], "metric_slic": [5, 36, 60, 61, 62, 63, 65], "pass_fail_threshold": [5, 36, 60, 61, 62, 63, 65], "pass_fail_threshold_fn": [5, 36, 60, 61, 62, 63, 65], "lambda": [5, 60, 61, 62, 63, 64, 65], "threshold": [5, 9, 36, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65, 67], "bool": [5, 11, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 31, 32, 36, 38, 39, 44, 45, 58, 60, 61, 62, 63, 65], "roc": [5, 46, 48, 60, 61, 62, 65], "roc_curv": [5, 60, 61, 62, 65], "roc_plot": [5, 60, 61, 62, 65], "roc_curve_comparison": [5, 60, 61, 62, 65], "quantit": [5, 36, 60, 61, 62, 63, 65, 68], "overall_perform": [5, 60, 61, 62, 65], "binaryprecisionrecallcurv": [5, 60, 61, 62, 65], "overall_performance_plot": [5, 60, 61, 62, 65], "metrics_valu": [5, 60, 61, 62, 65], "comparison": [5, 8, 60, 61, 62, 65], "ha": [5, 7, 9, 23, 36, 60, 61, 62, 63, 65], "sever": [5, 60, 61, 62, 63, 65], "multipl": [5, 8, 9, 18, 36, 44, 45], "could": [5, 60, 61, 62, 65], "one": [5, 8, 11, 17, 38, 39, 44, 45, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57], "default": [5, 11, 13, 15, 17, 19, 20, 21, 22, 23, 27, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65], "high": [5, 60, 61, 62, 63, 65], "level": [5, 9, 45, 60, 61, 62, 63, 65], "audienc": [5, 60, 61, 62, 65], "owner": [5, 8, 36, 60, 61, 62, 63, 65], "own": [5, 17, 60, 61, 62, 65], "made": [5, 36, 60, 61, 62, 65], "refer": [5, 6, 8, 36, 60, 61, 62, 63, 65], "relev": [5, 8, 36, 60, 61, 62, 65, 66], "store": [5, 60, 61, 62, 65], "regulatory_requir": [5, 60, 61, 62, 65], "regulatori": [5, 36, 60, 61, 62, 65], "requir": [5, 12, 36, 38, 39, 45, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65], "pass": [5, 15, 17, 27, 36, 38, 39, 44, 60, 61, 62, 65], "log_from_dict": [5, 36, 60, 61, 62, 63, 65], "model_detail": [5, 36, 60, 61, 62, 65], "log_descriptor": [5, 36, 60, 61, 62, 63, 65], "new": [5, 9, 11, 36, 60, 61, 62, 65], "attribut": [5, 8, 15, 17, 38, 39, 60, 61, 62, 65], "log_vers": [5, 36, 60, 61, 62, 65], "version_str": [5, 36, 60, 61, 62, 65], "str": [5, 11, 12, 15, 17, 19, 20, 21, 22, 23, 24, 27, 29, 36, 38, 39, 44, 45, 60, 61, 62, 65], "todai": [5, 60, 61, 62, 65], "initi": [5, 9, 38, 39, 47, 60, 61, 62, 65, 67], "releas": [5, 60, 61, 62, 65], "log_own": [5, 36, 60, 61, 62, 63, 65], "team": [5, 60, 61, 62, 65], "contact": [5, 36, 60, 61, 62, 63, 65], "vectorinstitut": [5, 60, 61, 62, 65], "github": [5, 60, 61, 62, 63, 65], "io": [5, 60, 61, 62, 65], "email": [5, 60, 61, 62, 63, 65], "ai": [5, 6, 60, 61, 62, 65], "log_licens": [5, 36, 60, 61, 62, 65], "apach": [5, 36, 60, 61, 62, 65], "log_refer": [5, 36, 60, 61, 62, 65], "scikit": [5, 61], "org": [5, 36, 61, 63], "stabl": [5, 60, 61, 62, 65], "modul": [5, 26, 35, 36, 44, 45, 60, 61, 62, 65], "sgdclassifi": [5, 60, 61], "e501": [5, 60, 61, 62, 65], "next": [5, 60, 61, 62, 65], "intend": [5, 60, 61, 62, 63, 65], "use_cas": [5, 60, 61, 62, 65], "These": [5, 60, 61, 62, 65], "primari": [5, 6, 36, 60, 61, 62, 63, 65], "downstream": [5, 60, 61, 62, 65], "scope": [5, 36, 60, 61, 62, 65], "fairness_assess": [5, 60, 61, 62, 65], "benefit": [5, 36, 60, 61, 62, 63, 65], "harm": [5, 36, 60, 61, 62, 63, 65], "taken": [5, 60, 61, 62, 65], "mitig": [5, 36, 60, 61, 62, 63, 65], "ethical_consider": [5, 60, 61, 62, 65], "associ": [5, 31, 60, 61, 62, 63, 65], "log_risk": [5, 36, 60, 61, 62, 63, 65], "hospit": [5, 27, 59, 60, 61, 62, 65], "clinician": [5, 6, 8, 60, 61, 62, 65], "log_us": [5, 36, 60, 61, 62, 63, 65], "engin": [5, 6, 60, 61, 62, 65], "log_use_cas": [5, 36, 60, 61, 62, 63, 65], "kind": [5, 36, 60, 61, 62, 63, 65], "pathologi": [5, 39, 60, 61, 67], "condit": [5, 60, 61], "log_fairness_assess": [5, 36, 60, 61, 62, 63, 65], "affected_group": [5, 36, 60, 61, 62, 63, 65], "e": [5, 8, 11, 12, 17, 19, 20, 31, 32, 33, 36, 44, 45, 60, 61, 62, 65], "g": [5, 8, 11, 12, 17, 19, 31, 32, 33, 36, 44, 45, 60, 61, 62, 65], "lead": [5, 60, 61, 62, 65], "wors": [5, 60, 61, 62, 65], "mitigation_strategi": [5, 36, 60, 61, 62, 63, 65], "retrain": [5, 60, 61, 62, 65], "decis": [5, 36, 48, 53, 55, 60, 61, 62, 65], "affect": [5, 30, 33, 36, 60, 61, 62, 65], "continu": [5, 45, 58, 60, 61, 62, 63, 65], "By": [5, 8, 60, 61, 62, 65], "save": [5, 36, 38, 39, 60, 61, 62, 65], "folder": [5, 60, 61, 62, 65], "cyclops_report": [5, 36, 60, 61, 62, 65], "current": [5, 36, 60, 61, 62, 65], "directori": [5, 36, 38, 39, 60, 61, 62, 65], "output_dir": [5, 36, 60, 61, 62, 65], "instanti": [5, 60, 61, 62, 65], "random": [5, 60, 61, 62, 64, 65], "seed": [5, 29, 60, 61, 62, 64, 65], "42": [5, 29, 61, 64], "synthetic_timestamp": [5, 36, 60, 61, 62, 63, 65], "date_rang": [5, 60, 61], "2020": [5, 17, 19, 61, 63], "period": [5, 60, 61, 67], "freq": [5, 60, 61], "_model_card": [5, 61, 62, 65], "overview": [5, 36, 60, 61, 62, 63, 65, 68], "none": [5, 11, 12, 13, 15, 17, 19, 27, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65], "report_path": [5, 60, 61, 62, 63, 65], "output_filenam": [5, 36, 60, 61, 62, 63, 65], "heart_failure_report_period": [5, 61], "last_n_ev": [5, 36, 60, 61], "3": [5, 31, 32, 36, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 66], "metric_sav": [5, 61], "rang": [5, 9, 17, 21, 48, 49, 53, 55, 61, 62, 63, 65], "len": [5, 53, 60, 61, 62, 63, 65], "quantitative_analysi": [5, 61, 62, 65], "performance_metr": [5, 61, 62, 65], "append": [5, 60, 61, 65], "clip": [5, 33, 61, 62, 65], "normal": [5, 33, 61, 62, 65], "deepcopi": [5, 60, 61, 62, 65], "els": [5, 55, 61, 63, 64], "rmtree": [5, 61, 62, 65], "re": 5, "abl": [5, 8, 9], "view": [5, 8, 9, 60, 61, 62, 63, 65, 67], "open": 6, "toolkit": 6, "scientist": [6, 8, 63], "artifici": 6, "intellig": 6, "design": 6, "help": [6, 8, 60, 61, 62, 63, 65], "research": [6, 60], "practition": 6, "adopt": 6, "vector": 6, "institut": 6, "trust": 6, "safeti": 6, "principl": 6, "focus": 6, "system": 6, "develop": [6, 8, 60, 61, 62, 63, 65], "clinic": [6, 26, 27, 60, 66], "applic": [6, 26, 27, 29, 48], "critic": 6, "goal": [6, 67], "transpar": 6, "heart": [6, 8, 59, 68], "failur": [6, 8, 59, 68], "time": [6, 8, 9, 11, 17, 27, 36, 60, 61, 62, 63, 65, 66], "poetri": 6, "contribut": 6, "pre": [6, 60, 61, 62, 65], "commit": 6, "hook": 6, "code": [6, 8, 9, 60, 61, 62, 65], "pycyclop": [7, 60], "mani": 7, "option": [7, 9, 11, 12, 13, 15, 17, 19, 20, 21, 22, 23, 27, 29, 36, 38, 39, 44, 45, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "function": [7, 10, 11, 13, 15, 16, 17, 18, 28, 36, 44, 45, 60, 61, 63], "monai": [7, 15], "librari": [7, 38, 39, 44, 45, 66, 67], "dicom": 7, "imag": [7, 14, 15, 33, 36, 39, 40, 44, 45, 59, 63, 64], "extra": [7, 8, 36], "note": [7, 11, 20, 36, 44, 60, 61, 62, 65], "xgboost": [7, 60, 62, 65], "allow": [7, 8, 9, 44, 45, 60, 61, 62, 65, 66], "torch": [7, 38, 39, 44, 45, 64], "pytorch": 7, "torchvis": [7, 63, 64], "torchxrayvis": [7, 63, 64], "transform": [7, 11, 38, 39, 48, 55, 60, 61, 62, 63, 64, 65], "alibi": 7, "explain": [7, 36], "detect": [7, 63, 66], "shift": [7, 26, 27, 29, 31, 32, 66], "technician": 8, "understand": [8, 60, 61, 62, 65], "better": [8, 9], "offer": [8, 60, 61, 62, 63, 65], "clear": 8, "effortlessli": 8, "incorpor": 8, "gener": [8, 9, 17, 27, 36, 55, 66, 67, 68], "figur": [8, 9, 36, 60, 61, 62, 65, 68], "provid": [8, 11, 17, 19, 22, 36, 44, 45, 53, 55, 58, 60, 61, 62, 63, 65], "pictur": 8, "everyon": 8, "spec": 8, "easi": [8, 60, 61, 62, 65], "interact": [8, 36, 62, 65], "subgroup": [8, 60, 61, 62, 63, 65, 68], "adjust": [8, 63], "displai": 8, "what": 8, "tell": 8, "u": [8, 59, 60, 61, 62, 65], "ad": [8, 9, 11, 29, 36, 38, 39, 60, 61, 62, 65, 68], "comprehens": 8, "color": [8, 9], "identif": 8, "signific": 8, "tailor": 8, "number": [8, 9, 11, 12, 13, 27, 31, 32, 36, 38, 39, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 62, 65, 66], "addition": [8, 63], "access": [8, 15], "brief": 8, "f1": [8, 46], "score": [8, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58], "correspond": [8, 9, 38, 39, 53], "conveni": 8, "timestamp": [8, 9, 11, 36, 62, 66], "shown": [8, 9], "ll": 8, "find": [8, 23, 38, 47, 48, 49, 50, 51, 52, 54, 56, 57, 63], "distribut": [8, 63, 67, 68], "part": 8, "zoom": 8, "pan": 8, "further": 8, "investig": 8, "chart": 8, "handi": 8, "tool": [8, 60, 61, 62, 63, 65], "compar": 8, "ag": [8, 9, 27, 64, 67, 68], "race": [8, 60, 65], "same": [8, 31, 44, 45], "isn": 8, "toward": 8, "against": 8, "particular": 8, "metadata": [8, 11, 15, 60, 61, 62, 63, 65], "extern": 8, "paper": 8, "entail": 8, "ethic": [8, 63], "card": [8, 36, 60, 61, 62, 65, 67, 68], "preprocess": [8, 60, 61, 67, 68], "log": [8, 36, 44, 60, 61, 62, 65, 67, 68], "modelcard": [8, 36, 68], "export": [8, 36, 60, 61, 62, 63, 65, 68], "accomplish": 9, "trend": [9, 36], "latest": [9, 11], "everytim": 9, "entri": [9, 36, 58], "do": [9, 17, 60, 61, 62, 63, 65], "repeatedli": 9, "dai": [9, 17, 19, 60, 62, 65, 67], "week": 9, "month": [9, 17, 19, 27], "At": 9, "top": [9, 47, 49, 50, 51, 52, 54, 56, 57, 58], "glanc": [9, 60, 61, 62, 63, 65], "left": [9, 36, 65], "minimum": [9, 17, 21, 36], "accept": [9, 11], "red": 9, "everyth": 9, "green": 9, "prespect": 9, "interv": 9, "again": 9, "past": 9, "slider": 9, "aggfunc": [11, 62, 65], "timestamp_col": [11, 62], "time_bi": [11, 62], "agg_bi": [11, 62], "timestep_s": 11, "window_dur": [11, 62], "window_start_tim": [11, 62], "window_stop_tim": 11, "imput": [11, 60, 61, 62, 65], "agg_meta_for": 11, "transformermixin": 11, "equal": [11, 17, 21, 45], "space": [11, 55], "seri": [11, 13, 38, 62], "comput": [11, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 67], "expens": [11, 44, 45], "sparingli": 11, "map": [11, 17, 18, 38, 39, 63], "col_nam": 11, "return": [11, 12, 13, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 62, 65], "determin": [11, 47, 48, 49, 53, 55, 58], "bucket": [11, 13], "hour": [11, 17, 19, 62], "timestep": [11, 12, 13], "float": [11, 13, 20, 21, 30, 31, 32, 33, 34, 36, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "window": [11, 13, 66], "stop": [11, 65], "window_tim": 11, "aggregatedimput": 11, "num_timestep": [11, 12, 13], "median": 11, "std": 11, "var": 11, "min": 11, "max": [11, 12, 65], "sum": [11, 57, 58, 62, 63, 65], "init": 11, "__init__": [11, 38, 39], "paramet": [11, 12, 13, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65, 68], "set_output": 11, "introduc": 11, "polar": 11, "configur": [11, 38, 39, 44, 60, 61, 62, 65], "unchang": 11, "4": [11, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 62, 65, 66], "self": [11, 15], "estim": [11, 38, 47, 48, 50, 51, 53, 55, 57], "include_timestep_start": 11, "placehold": [11, 31, 34], "compat": [11, 60, 61, 62, 65], "whether": [11, 15, 17, 27, 36, 44, 45, 58, 62, 65, 67], "var_nam": 12, "value_nam": 12, "strategi": [12, 36, 60, 61, 62, 63, 65], "sort": [12, 60, 61, 62, 65], "pose": 12, "tabular": [12, 38, 39, 59], "timeseri": 12, "origin": [12, 27], "fake": 12, "tempor": 12, "val": [13, 44], "fill_nan": 13, "forward": 13, "given": [13, 19, 21, 22, 23, 24, 36, 38, 39, 47, 50, 51, 52, 54, 56, 57, 58], "treat": [13, 45], "occur": 13, "regular": 13, "between": [13, 17, 45, 50, 55, 58], "accordingli": 13, "nan": [13, 20, 44, 45, 60, 67], "integ": [13, 17, 36, 44, 45], "total": [13, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 65], "consid": [13, 20, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 62], "remain": [13, 63], "dimension": [13, 66], "arrai": [13, 15, 31, 32, 34, 38, 39, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 63, 65], "ndarrai": [13, 15, 24, 30, 31, 32, 33, 34, 38, 39, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "medic": [14, 15, 40, 63, 65, 67], "mode": 15, "decod": 15, "id": [15, 27, 60, 61, 65], "reader": 15, "itkread": 15, "suffix": 15, "jpg": 15, "read": 15, "union": [15, 17, 20, 23, 24, 36, 38, 39, 44, 45, 48, 53], "imageread": 15, "byte": 15, "image_path": 15, "image_byt": 15, "__call__": 15, "cast_storag": 15, "storag": 15, "cast": 15, "arrow": 15, "pyarrow": [15, 18, 19, 20, 21, 22, 23, 25, 62], "rtype": 15, "structarrai": 15, "pa": 15, "must": [15, 19, 36, 44, 45, 49], "struct": 15, "doesn": 15, "matter": 15, "arg": 15, "stringarrai": 15, "listarrai": 15, "decode_exampl": 15, "token_per_repo_id": 15, "serial": 15, "privat": 15, "hub": 15, "repo_id": 15, "deseri": 15, "embed_storag": 15, "emb": 15, "encode_exampl": 15, "encod": 15, "flatten": 15, "state": [15, 53], "itself": 15, "otherwis": [15, 24, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58], "tupl": [15, 17, 27, 38, 39, 53, 55], "classlabel": [15, 60, 61, 62, 65], "translat": 15, "translationvariablelanguag": 15, "sequenc": [15, 44], "array2d": 15, "array3d": 15, "array4d": 15, "array5d": 15, "audio": 15, "factori": [17, 63], "include_overal": [17, 60, 61], "column_nam": [17, 19, 20, 21, 22, 23, 63], "exact": [17, 23], "support": [17, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 65, 66], "00": 17, "numer": [17, 21, 60, 61, 62, 65], "inf": [17, 21], "boolean": [17, 18, 19, 20, 21, 22, 23, 25], "greater": [17, 60, 65], "maximum": [17, 21, 48], "12": [17, 62, 65], "31": 17, "negat": [17, 19, 20, 21, 22, 23, 63], "flag": [17, 62], "keep_nul": [17, 19, 21, 22, 23], "keep": [17, 44, 45, 65], "conjunct": [17, 63], "r": 17, "itertool": 17, "_before_": 17, "regist": 17, "_registri": 17, "callabl": [17, 18, 36, 45], "feature_1": 17, "feature_2": 17, "feature_3": 17, "value_1": 17, "value_2": 17, "60": [17, 60], "2022": [17, 62, 63, 65], "6": [17, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64], "2000": 17, "2010": 17, "slice_func": 17, "someth": 17, "filter": [17, 19, 20, 21, 22, 23, 44, 45, 62, 63, 65], "non_nul": 17, "wai": 17, "compound": 17, "add_slice_spec": 17, "get_slic": 17, "registri": [17, 60, 61, 62, 65], "slice_funct": 18, "bitwis": 18, "AND": 18, "tabl": [18, 19, 20, 21, 22, 23, 25], "lazybatch": [18, 19, 20, 21, 22, 23, 25], "signatur": 18, "kwarg": [18, 29, 38, 39], "batch": [19, 20, 21, 22, 23, 25, 27, 38, 39, 44, 45, 62, 63], "compon": [19, 60, 61], "rais": [19, 21, 22, 36, 38, 39, 44, 45, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60], "typeerror": [19, 21, 22, 36, 45], "valueerror": [21, 36, 38, 39, 44, 45, 47, 49, 51, 52, 53, 54, 55, 56, 57, 58, 60], "substr": 22, "datetime64": 24, "shift_typ": [27, 29], "shift_id": [27, 64], "induc": [27, 29], "synthet": [27, 29, 36, 65, 66, 67], "categor": [27, 60, 61, 62, 65], "loader": [27, 63, 64], "load_nihcxr": [27, 63, 64], "mnt": [27, 63, 64], "nihcxr": [27, 63, 66], "hospital_typ": 27, "hospital_type_1": 27, "hospital_type_2": 27, "hospital_type_3": 27, "hospital_type_4": 27, "hospital_type_5": 27, "ds_sourc": [27, 64], "ds_target": [27, 64], "apply_shift": [27, 29, 64], "custom": [27, 36, 64], "batch_siz": [27, 38, 39, 44, 45, 60, 61, 62, 65], "1000": [27, 38, 44, 45, 60, 63], "num_proc": [27, 64], "size": [27, 38, 39, 44, 45, 60, 61, 62, 63, 65], "syntheticshiftappl": 28, "load_diabet": 29, "return_x_i": 29, "from_dict": 29, "gn_shift": 29, "x_shift": 29, "ko_shift": 29, "cp_shift": 29, "mfa_shift": 29, "bn_shift": 29, "tolerance_shift": 29, "ds_shift": 29, "nois": [29, 30, 33], "prob": 30, "delta": [30, 33, 34], "matrix": [30, 31, 32, 33, 34, 65], "covari": [30, 31, 32, 33, 34], "p": [30, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 63], "control": [30, 45], "fraction": [30, 33, 34, 65], "dtype": [30, 31, 32, 33, 34, 62], "n_shuffl": [31, 32], "25": [31, 32, 48, 53, 55, 57, 58, 62, 64, 65], "keep_rows_const": 31, "repermute_each_column": 31, "multiwai": 31, "swap": [31, 32], "cl": [31, 32], "floatnumpi": 31, "shuffl": [31, 32], "permut": 31, "shift_class": [32, 34], "rank": 32, "changepoint": 32, "x_ref": 32, "y_ref": 32, "noise_amt": 33, "gaussian": 33, "standard": [33, 36, 60, 61, 62, 65], "deviat": [33, 36], "divid": 33, "255": [33, 63, 64], "imbal": [34, 47, 48, 49, 50, 51, 52, 54, 56, 57], "sampl": [34, 52, 54, 56], "serv": 36, "interfac": 36, "template_path": 36, "save_json": 36, "mean_std_min_ev": 36, "jinja2": 36, "most": [36, 61], "recent": 36, "classmethod": 36, "from_json_fil": 36, "log_cit": [36, 63], "content": 36, "repres": [36, 63], "keyerror": 36, "bibtex": 36, "plain": 36, "text": [36, 63], "context": 36, "homepag": 36, "unknown": 36, "unlicens": 36, "proprietari": 36, "assertionerror": [36, 53, 55], "descriptor": 36, "pydant": 36, "basemodel": 36, "subclass": 36, "long": 36, "conflict": 36, "model_card": 36, "tradeoff": [36, 63], "trade": 36, "off": 36, "interpret": 36, "consider": [36, 60, 61, 62, 63, 65, 68], "relat": 36, "log_imag": 36, "img_path": 36, "exist": [36, 38, 39], "full": 36, "whole": [36, 60, 61, 62, 65], "blank": 36, "instead": 36, "log_model_paramet": [36, 60, 61, 62, 65], "param": [36, 60, 61, 62, 65], "role": 36, "slash": 36, "analysis_typ": 36, "decision_threshold": 36, "liter": [36, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "fail": 36, "log_regul": 36, "regul": 36, "compli": 36, "usecas": 36, "semant": 36, "v1": 36, "dt_date": 36, "dt_datetim": 36, "unix": 36, "yyyi": 36, "mm": 36, "dd": 36, "hh": 36, "ss": 36, "ffffff": 36, "z": 36, "summar": 36, "task_featur": [38, 39, 60, 61, 62, 65], "task_target": [38, 39, 60, 61, 62, 65], "basetask": [38, 39], "add_model": [38, 39], "wrappedmodel": [38, 39], "properti": [38, 39], "data_typ": [38, 39], "prediction_column_prefix": [38, 39, 60, 61, 62, 65], "splits_map": [38, 39], "remove_column": [38, 39, 44, 45, 63], "fairness_config": [38, 39, 44, 60, 61, 62, 65], "override_fairness_metr": [38, 39, 44, 60, 61, 62, 65], "array_lib": [38, 39, 44, 45], "datasetdict": [38, 39, 44], "columntransform": [38, 60, 61, 62, 65], "slicingconfig": 38, "config": 38, "default_max_batch_s": 38, "unnecessari": [38, 39], "fairnessconfig": [38, 39, 44, 60, 61, 62, 65], "overridden": [38, 39, 44], "cupi": [38, 39, 44, 45], "get_model": [38, 39], "list_model": [38, 39, 60, 61, 62, 65], "list_models_param": [38, 39, 60, 61, 62, 65], "load_model": [38, 39], "filepath": [38, 39], "pretrain": [38, 39, 63], "models_count": [38, 39], "proba": [38, 60, 61, 62, 65], "mortal": [38, 59], "notfittederror": 38, "save_model": [38, 39], "destin": [38, 39], "parent": [38, 39], "keyword": [38, 39, 44, 45], "best_model_param": [38, 60, 61, 62, 65], "pipelin": [38, 60, 61, 62, 65], "best": [38, 60, 61, 62, 65], "64": 39, "compos": [39, 60, 61, 62, 63, 64, 65], "represent": 39, "packag": [40, 41, 42, 43, 60, 61, 62, 63, 65], "consist": [44, 45], "ignore_column": [44, 63], "raise_on_empty_slic": [44, 45], "load_dataset_kwarg": 44, "load_dataset": 44, "multi": [44, 63], "while": 44, "memori": [44, 45], "entir": [44, 60, 61, 62, 65], "chosen": 44, "eval": 44, "dev": 44, "empti": [44, 45], "warn": [44, 47, 49, 50, 51, 52, 54, 56, 57], "pariti": 45, "group_valu": 45, "group_bin": [45, 61, 62, 65], "group_base_valu": [45, 60, 61, 62, 65], "compute_optimal_threshold": 45, "metric_kwarg": 45, "multilabel": [45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 67], "limit": [45, 60, 61, 62, 63, 65], "slow": 45, "larg": 45, "denomin": 45, "linspac": 45, "monoton": [45, 49, 55], "necessari": 45, "rel": 45, "small": [45, 60], "32": 45, "avoid": 45, "encount": [45, 62, 65], "nest": 45, "second": 45, "third": 45, "omit": 45, "runtimeerror": 45, "averag": [46, 47, 48, 50, 51, 52, 54, 56, 57, 60, 61, 62, 63], "beta": 46, "precisionrecallcurv": 46, "statscor": 46, "multiclass": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "One": [47, 48, 51, 52, 54, 55, 62, 63, 65], "pos_label": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "num_class": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65], "decid": [47, 50, 51, 52, 54, 56, 57], "top_k": [47, 49, 50, 51, 52, 54, 56, 57, 58], "logit": [47, 50, 51, 52, 53, 54, 56, 57, 58], "k": [47, 50, 51, 52, 54, 56, 57, 58, 64], "num_label": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 63], "micro": [47, 48, 49, 50, 51, 52, 54, 56, 57], "macro": [47, 48, 49, 50, 51, 52, 54, 56, 57], "weight": [47, 48, 49, 50, 51, 52, 54, 56, 57, 63, 64, 65], "global": [47, 48, 49, 50, 51, 52, 54, 56, 57], "unweight": [47, 48, 49, 50, 51, 52, 54, 56, 57], "alter": [47, 50, 51, 52, 54, 56, 57], "zero_divis": [47, 49, 50, 51, 52, 54, 56, 57], "zero": [47, 49, 50, 51, 52, 54, 56, 57], "divis": [47, 49, 50, 51, 52, 54, 56, 57], "act": [47, 49, 50, 51, 52, 54, 56, 57, 58], "75": [47, 48, 49, 53, 55, 56, 57, 60, 61], "reset_st": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "05": [47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58], "95": [47, 48, 50, 52, 53, 55], "zip": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "update_st": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "66666667": [47, 50, 53, 54, 55, 56, 57], "sigmoid": [47, 48, 51, 53, 55, 57, 58], "875": 47, "problem": [47, 53, 56, 58, 67], "highest": [47, 52, 54, 56, 57], "binar": [47, 48, 50, 55, 58], "correct": [47, 48, 50, 51, 52, 53, 54, 55, 56, 57], "per": [47, 62, 65], "npt": [47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58], "arraylik": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "acc_scor": 47, "9": [47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58], "5555555555555555": 47, "shape": [47, 48, 50, 51, 52, 53, 54, 56, 57, 58, 63, 64], "num_sampl": 47, "flot": 47, "6666666666666666": [47, 50, 52, 53], "max_fpr": 48, "partial": [48, 63], "auc": 48, "automat": [48, 49, 53, 55], "35": [48, 49, 53, 55, 56, 57, 63, 64], "6111111111111112": 48, "89": [48, 55], "06": [48, 55, 63], "94": 48, "22222222": 48, "625": [48, 51, 57], "aucroc": 48, "auroc_scor": 48, "thei": [48, 53, 55], "via": 48, "softmax": [48, 53, 55], "33333333": [48, 53, 54, 55, 56], "5833333333333333": 49, "1d": [49, 55], "increas": [49, 55], "85714286": 50, "8333333333333334": [51, 52, 54, 56], "9090909090909091": 51, "83333333": 51, "55555556": [51, 57], "90909091": 51, "85": [51, 52, 54, 56, 60, 61, 65], "7142857142857143": 51, "ratio": [52, 54, 56, 57, 65], "observ": [52, 65, 67], "precision_scor": 52, "22": [52, 58], "84": 52, "73": [52, 65], "33": 52, "92": 52, "n": [52, 54, 56, 60, 62], "l": [52, 54, 56, 60], "presenc": [52, 54, 56, 63], "suniqu": 53, "prcurv": 53, "45": [53, 56, 57], "42857143": 53, "375": 53, "multilaabel": 53, "prec": 53, "3333333333333333": [53, 54, 56], "rec": 53, "binary_precision_recall_curv": [53, 60, 61, 62, 65], "autofunct": [53, 54], "element": 53, "tp": [54, 56], "fn": [54, 56], "intuit": [54, 56], "abil": [54, 56, 60, 61, 62, 65], "recall_scor": 54, "precision_recal": 54, "rest": [54, 56], "evenli": 55, "fpr": [55, 60, 61, 62, 65], "tpr": 55, "02": 55, "03": 55, "sensitv": 56, "55": 56, "65": [56, 63, 64], "57142857": 57, "prior": 58, "classwis": 58, "labelwis": 58, "16": 58, "26": 58, "58": 58, "61": 58, "71": 58, "09": [58, 62, 65], "20": [58, 60, 61, 62, 63, 65], "82": 58, "special": 58, "statist": [58, 60, 61, 62, 63, 65, 66], "n_class": 58, "n_label": 58, "assum": 58, "nadarrai": 58, "mimiciv": [59, 62], "synthea": [59, 65], "prolong": [59, 62], "length": [59, 62], "stai": [59, 62], "diabet": [59, 60], "130": [59, 60], "1999": [59, 60], "2008": [59, 60], "readmiss": 59, "nih": [59, 63, 64], "chest": 59, "rai": 59, "notebook": [60, 61, 62, 63, 65], "showcas": [60, 61, 62, 65, 67], "formul": [60, 61, 62, 65], "earli": 60, "discharg": 60, "ucimlrepo": 60, "e402": [60, 61, 62, 63, 65], "simpleimput": [60, 61, 62, 65], "onehotencod": [60, 61, 62, 65], "fetch_ucirepo": 60, "tabularfeatur": [60, 61, 62, 65], "binary_npv": [60, 61], "binary_ppv": [60, 61], "binary_roc": [60, 61], "catalog": [60, 61, 62, 65], "create_model": [60, 61, 62, 65], "binarytabularclassificationtask": [60, 61, 62, 65], "technic": [60, 61, 62, 63, 65], "architectur": [60, 61, 62, 63, 65], "involv": [60, 61, 62, 63, 65], "end": [60, 61, 62, 63, 65], "progress": [60, 61, 62, 63, 65], "subject": [60, 61, 62, 63, 65], "nan_threshold": [60, 61, 67], "train_siz": [60, 61, 62, 65], "eval_num": 60, "diabetes_130_data": 60, "296": 60, "variabl": [60, 67], "def": [60, 62, 65], "transform_label": 60, "readmit": 60, "NO": 60, "unexpect": 60, "choos": 60, "features_to_remov": 60, "col": [60, 62], "features_list": [60, 61, 62, 65], "essenti": [60, 61, 62, 65], "tab_featur": [60, 61, 62, 65], "reset_index": [60, 61, 62, 65], "numeric_transform": [60, 61, 62, 65], "binary_transform": [60, 61, 62, 65], "most_frequ": [60, 61, 62, 65], "numeric_featur": [60, 61, 62, 65], "features_by_typ": [60, 61, 62, 65], "numeric_indic": [60, 61, 62, 65], "get_loc": [60, 61, 62, 65], "binary_featur": [60, 61, 62, 65], "ordinal_featur": [60, 61, 62], "ordin": [60, 61, 62], "medical_specialti": 60, "diag_1": 60, "diag_2": 60, "diag_3": 60, "binary_indic": [60, 61, 62, 65], "ordinal_indic": [60, 61, 62], "num": [60, 61, 62, 65], "onehot": [60, 61, 62, 65], "handle_unknown": [60, 61, 62, 65], "sparse_output": 60, "remaind": [60, 61, 62, 65], "passthrough": [60, 61, 62, 65], "abstract": 60, "articl": 60, "strack2014impact": 60, "impact": 60, "hba1c": 60, "000": 60, "record": 60, "strack": 60, "beata": 60, "deshazo": 60, "jonathan": 60, "gen": 60, "chri": 60, "olmo": 60, "juan": 60, "ventura": 60, "sebastian": 60, "cio": 60, "krzysztof": 60, "j": 60, "clore": 60, "john": 60, "journal": 60, "biom": 60, "intern": 60, "volum": [60, 65], "2014": 60, "hindawi": 60, "repository_url": 60, "power": [60, 61, 62, 65], "cast_column": [60, 61, 62, 65], "stratify_by_column": [60, 61, 62, 65], "straightforward": [60, 61, 62, 65], "maintain": [60, 61, 62, 65], "line": [60, 61, 62, 65], "sgd": [60, 61], "logisit": [60, 61, 62, 65], "regress": [60, 61, 62, 65], "xgb_classifi": [60, 62, 65], "123": [60, 61, 62, 65], "encapsul": [60, 61, 62, 65], "cohes": [60, 61, 62, 65], "smooth": [60, 61, 62, 65], "manag": [60, 61, 62, 65], "readmission_prediction_task": 60, "hyperparamet": [60, 61, 62, 65], "search": [60, 61, 62, 65], "grid": [60, 61, 62, 65], "n_estim": [60, 62, 65], "100": [60, 61, 62, 63, 65], "250": [60, 62, 65], "500": [60, 62, 65], "learning_r": [60, 61, 62, 65], "max_depth": [60, 62, 65], "reg_lambda": [60, 62, 65], "colsample_bytre": [60, 62, 65], "scale_pos_weight": 60, "model_param": [60, 61, 62, 65], "only_predict": [60, 61, 62, 65], "prediction_df": [60, 61], "y_prob": [60, 61], "y_pred_i": [60, 61], "y_true": [60, 61], "perspect": [60, 61, 62, 65], "binary_average_precis": [60, 61, 62], "binary_specif": [60, 61, 62, 65], "binary_sensit": [60, 61, 62, 65], "fnr": [60, 61, 62, 65], "ber": [60, 61, 62, 65], "fairness_metric_collect": [60, 61, 62, 65], "40": [60, 61, 62, 65], "dataset_with_pr": [60, 61, 62, 65], "results_femal": [60, 61], "_": [60, 61], "femal": [60, 61, 63, 64, 65], "model_for_pr": [60, 61, 62, 65], "results_female_flat": [60, 61], "w505": [60, 61, 62], "pr_curv": [60, 61, 62], "average_precis": [60, 61, 62], "pr_plot": [60, 61, 62], "precision_recall_curve_comparison": [60, 61, 62], "pred_prob": [60, 61], "true_label": [60, 61], "ppv": [60, 61, 63], "zeros_lik": [60, 61], "npv": [60, 61, 63], "enumer": [60, 61, 62, 63], "runway_plot": [60, 61], "threshperf": [60, 61], "calibr": [60, 61], "calibration_plot": [60, 61], "y_true_col": [60, 61], "y_prob_col": [60, 61], "group_col": [60, 61], "reformat": [60, 61, 62, 65], "fairness_metr": [60, 61, 62, 65], "group_siz": [60, 61, 62, 65], "pop": [60, 61, 62, 65], "fairness_plot": [60, 61, 62, 65], "metrics_comparison_scatt": [60, 61, 62, 65], "some": [60, 61, 62, 65], "readthedoc": [60, 62, 65], "en": [60, 62, 65], "python_api": [60, 62, 65], "2024": 60, "w": [60, 67], "readmission_report_period": 60, "popular": 61, "load_datafram": 61, "usernam": 61, "file_format": 61, "li": 61, "et": 61, "al": 61, "chestpaintyp": 61, "restingbp": 61, "cholesterol": 61, "fastingb": 61, "restingecg": 61, "maxhr": 61, "exerciseangina": 61, "oldpeak": 61, "st_slope": 61, "sgd_classifi": 61, "verbos": 61, "class_weight": 61, "balanc": 61, "heart_failure_prediction_task": 61, "alpha": 61, "0001": 61, "001": 61, "invscal": 61, "adapt": 61, "eta0": 61, "roc_auc": 61, "die": 62, "admiss": 62, "24": 62, "cycqueri": [62, 65], "op": [62, 65], "qo": [62, 65], "graph_object": [62, 65], "mimicivqueri": 62, "imblearn": 62, "over_sampl": 62, "smote": 62, "imbpipelin": 62, "aggreg": 62, "restrict_timestamp": 62, "normalize_nam": 62, "add_years_approxim": 62, "deathtim": 62, "lab": [62, 63], "event": 62, "merg": [62, 65], "querier": [62, 65], "dbm": [62, 65], "postgresql": [62, 65], "port": [62, 65], "5432": [62, 65], "host": [62, 65], "localhost": [62, 65], "postgr": [62, 65], "password": [62, 65], "pwd": [62, 65], "get_encount": [62, 65], "mimiciv_hosp": 62, "drop_op": 62, "languag": 62, "marital_statu": 62, "edregtim": 62, "edouttim": 62, "patient_encount": [62, 65], "subject_id": 62, "admittim": 62, "dt": 62, "anchor_year": 62, "anchor_ag": 62, "dischtim": 62, "anchor_year_differ": 62, "hadm_id": 62, "admission_loc": 62, "admission_typ": 62, "insur": 62, "hospital_expire_flag": 62, "compute_mortality_outcom": 62, "death": 62, "invalid": 62, "isna": 62, "shorter": 62, "di": 62, "mortality_outcom": 62, "int64": 62, "loc": 62, "get_labev": 62, "labev": 62, "index_col": 62, "batch_mod": 62, "process_labev": 62, "revers": 62, "deidentifi": 62, "charttim": 62, "start_timestamp": 62, "set_index": 62, "renam": [62, 65], "mean_aggreg": 62, "valuenum": 62, "means_df": 62, "batch_num": 62, "labevents_batch": 62, "pivot": 62, "add_prefix": [62, 65], "lab_": 62, "break": 62, "run_queri": [62, 65], "cohort": [62, 65], "null_count": [62, 65], "isnul": [62, 65], "bar": [62, 63, 65], "height": 62, "600": 62, "respect": [62, 65], "final": [62, 63, 65], "larger": [62, 65], "thresh_nan": [62, 65], "dropna": [62, 65], "thresh": [62, 65], "heavili": 62, "unbalanc": 62, "preprocessor_pipelin": 62, "oversampl": 62, "xgbclassifi": [62, 65], "mortality_task": 62, "80": [62, 63, 65], "__rsub__": 62, "implement": [62, 67], "mortality_report_period": 62, "functool": 63, "densenet": [63, 64], "filter_valu": 63, "lambdad": [63, 64], "resiz": [63, 64], "apply_transform": 63, "wrapper": 63, "ptmodel": 63, "clinical_dataset": [63, 64], "nih_d": [63, 64], "spatial_s": [63, 64], "224": [63, 64], "allow_missing_kei": [63, 64], "func": [63, 64], "1024": [63, 64], "newaxi": [63, 64], "densenet121": [63, 64], "res224": [63, 64], "feature_column": [63, 64], "No": 63, "multilabel_ppv": 63, "multilabel_npv": 63, "multilabel_specif": 63, "multilabel_sensit": 63, "nih_eval_results_gend": 63, "19": 63, "nih_eval_results_ag": 63, "male": [63, 64], "showlegend": 63, "slice_": 63, "model_for_predict": 63, "itr": 63, "multilabelppv": 63, "among": 63, "multilabelnpv": 63, "multilabelsensit": 63, "multilabelspecif": 63, "isinst": 63, "112": [63, 67], "120": [63, 67], "frontal": [63, 67], "805": [63, 67], "fourteen": 63, "mine": 63, "radiolog": 63, "atelectasi": 63, "cardiomegali": 63, "effus": 63, "infiltr": 63, "mass": [63, 65], "nodul": 63, "pneumonia": 63, "pneumothorax": 63, "consolid": 63, "edema": 63, "emphysema": 63, "fibrosi": 63, "pleural": 63, "thicken": 63, "hernia": 63, "arxiv": 63, "ab": 63, "2111": 63, "00595": 63, "inproceed": 63, "cohen2022xrv": 63, "cohen": 63, "joseph": 63, "paul": 63, "viviano": 63, "bertin": 63, "morrison": 63, "torabian": 63, "parsa": 63, "guarrera": 63, "matteo": 63, "lungren": 63, "matthew": 63, "chaudhari": 63, "akshai": 63, "brook": 63, "rupert": 63, "hashir": 63, "mohammad": 63, "bertrand": 63, "hadrien": 63, "booktitl": 63, "deep": 63, "url": 63, "mlmed": 63, "arxivid": 63, "cohen2020limit": 63, "On": 63, "cross": 63, "domain": 63, "autom": [63, 65], "2002": 63, "02497": 63, "medicin": 63, "josephpcohen": 63, "radiologist": 63, "inabl": 63, "poor": 63, "qualiti": 63, "artifact": 63, "geograph": 63, "region": 63, "divers": 63, "regularli": 63, "updat": 63, "human": 63, "expertis": 63, "address": 63, "rare": 63, "nihcxr_report_period": 63, "2023": 63, "clinicalshiftappl": 64, "detector": 64, "reductor": 64, "tstester": 64, "plot_drift_experi": 64, "plot_drift_timeseri": 64, "manual_se": 64, "shifter": 64, "source_d": 64, "target_d": 64, "dr_method": 64, "bbse": 64, "soft": 64, "txrv": 64, "ae": 64, "sensitivity_test": 64, "tester": 64, "tester_method": 64, "source_sample_s": 64, "target_sample_s": 64, "num_run": 64, "detect_shift": 64, "chexpert": 64, "chex": 64, "padchest": 64, "pc": 64, "source_slic": 64, "target_slic": 64, "18": 64, "rolling_window_drift": 64, "timestamp_column": 64, "window_s": 64, "4w": 64, "longer": 65, "v3": 65, "etl": 65, "datasetqueri": 65, "num_dai": 65, "synthea_demo": 65, "nativ": 65, "sequenti": 65, "patient_id": 65, "birthdat": 65, "ethnic": 65, "isout": 65, "encounter_id": 65, "extracttimestampcompon": 65, "start_year": 65, "birthdate_year": 65, "addcolumn": 65, "new_col_label": 65, "lo": 65, "conditiongreaterthan": 65, "conditionlessthan": 65, "get_observ": 65, "conditionin": 65, "laboratori": 65, "vital": 65, "conditionequ": 65, "groupby_op": 65, "groupbyaggreg": 65, "n_ob": 65, "observations_count": 65, "observations_stat": 65, "pivot_t": 65, "obs_": 65, "get_med": 65, "n_med": 65, "get_procedur": 65, "procedur": [65, 67], "n_procedur": 65, "cohort_queri": 65, "to_merg": 65, "extend": 65, "to_merge_df": 65, "length_of_stai": 65, "length_of_stay_count": 65, "length_of_stay_kei": 65, "obs_alanin": 65, "aminotransferas": 65, "enzymat": 65, "serum": 65, "plasma": 65, "obs_albumin": 65, "obs_alkalin": 65, "phosphatas": 65, "obs_aspart": 65, "obs_bilirubin": 65, "obs_bodi": 65, "obs_calcium": 65, "obs_carbon": 65, "dioxid": 65, "mole": 65, "obs_chlorid": 65, "obs_creatinin": 65, "obs_diastol": 65, "blood": 65, "pressur": 65, "obs_erythrocyt": 65, "width": 65, "obs_ferritin": 65, "obs_glomerular": 65, "filtrat": 65, "sq": 65, "obs_glucos": 65, "obs_hematocrit": 65, "obs_hemoglobin": 65, "obs_leukocyt": 65, "obs_mch": 65, "entit": 65, "obs_mchc": 65, "obs_mcv": 65, "obs_oxygen": 65, "satur": 65, "arteri": 65, "obs_platelet": 65, "obs_potassium": 65, "obs_protein": 65, "obs_sodium": 65, "obs_systol": 65, "obs_troponin": 65, "cardiac": 65, "obs_urea": 65, "nitrogen": 65, "los_task": 65, "binary_confusion_matrix": 65, "rsub": 65, "binaryconfusionmatrix": 65, "confus": 65, "confusion_matrix": 65, "conf_plot": 65, "binaryroccurv": 65, "length_of_stay_report_period": 65, "drift": 66, "experi": 66, "reduct": 66, "techniqu": 66, "roll": 66, "biweekli": 66, "preprocessor": 67, "creation": 67, "ehr": 67, "site": 67, "icu": 67, "queri": 67, "47": 67, "histor": 67, "capabl": 68}, "objects": {"cyclops": [[40, 0, 0, "-", "data"], [41, 0, 0, "-", "monitor"], [42, 0, 0, "-", "report"], [43, 0, 0, "-", "tasks"]], "cyclops.data": [[10, 0, 0, "-", "aggregate"], [40, 0, 0, "-", "features"], [16, 0, 0, "-", "slicer"]], "cyclops.data.aggregate": [[11, 1, 1, "", "Aggregator"], [12, 4, 1, "", "tabular_as_aggregated"], [13, 4, 1, "", "timestamp_ffill_agg"]], "cyclops.data.aggregate.Aggregator": [[11, 2, 1, "", "__init__"], [11, 3, 1, "", "agg_by"], [11, 3, 1, "", "agg_meta_for"], [11, 3, 1, "", "aggfuncs"], [11, 2, 1, "", "fit"], [11, 2, 1, "", "fit_transform"], [11, 3, 1, "", "imputer"], [11, 3, 1, "", "num_timesteps"], [11, 2, 1, "", "set_output"], [11, 3, 1, "", "time_by"], [11, 3, 1, "", "timestamp_col"], [11, 3, 1, "", "timestep_size"], [11, 2, 1, "", "transform"], [11, 3, 1, "", "window_duration"], [11, 3, 1, "", "window_start_time"], [11, 3, 1, "", "window_stop_time"], [11, 3, 1, "", "window_times"]], "cyclops.data.features": [[14, 0, 0, "-", "medical_image"]], "cyclops.data.features.medical_image": [[15, 1, 1, "", "MedicalImage"]], "cyclops.data.features.medical_image.MedicalImage": [[15, 2, 1, "", "__call__"], [15, 2, 1, "", "cast_storage"], [15, 2, 1, "", "decode_example"], [15, 2, 1, "", "embed_storage"], [15, 2, 1, "", "encode_example"], [15, 2, 1, "", "flatten"]], "cyclops.data.slicer": [[17, 1, 1, "", "SliceSpec"], [18, 4, 1, "", "compound_filter"], [19, 4, 1, "", "filter_datetime"], [20, 4, 1, "", "filter_non_null"], [21, 4, 1, "", "filter_range"], [22, 4, 1, "", "filter_string_contains"], [23, 4, 1, "", "filter_value"], [24, 4, 1, "", "is_datetime"], [25, 4, 1, "", "overall"]], "cyclops.data.slicer.SliceSpec": [[17, 3, 1, "", "_registry"], [17, 2, 1, "", "add_slice_spec"], [17, 3, 1, "", "column_names"], [17, 2, 1, "", "get_slices"], [17, 3, 1, "", "include_overall"], [17, 2, 1, "", "slices"], [17, 3, 1, "", "spec_list"], [17, 3, 1, "", "validate"]], "cyclops.evaluate.evaluator": [[44, 4, 1, "", "evaluate"]], "cyclops.evaluate.fairness.evaluator": [[45, 4, 1, "", "evaluate_fairness"]], "cyclops.evaluate.metrics": [[48, 1, 1, "", "AUROC"], [47, 1, 1, "", "Accuracy"], [48, 1, 1, "", "BinaryAUROC"], [47, 1, 1, "", "BinaryAccuracy"], [49, 1, 1, "", "BinaryAveragePrecision"], [50, 1, 1, "", "BinaryF1Score"], [51, 1, 1, "", "BinaryFbetaScore"], [52, 1, 1, "", "BinaryPrecision"], [53, 1, 1, "", "BinaryPrecisionRecallCurve"], [55, 1, 1, "", "BinaryROCCurve"], [54, 1, 1, "", "BinaryRecall"], [56, 1, 1, "", "BinarySensitivity"], [57, 1, 1, "", "BinarySpecificity"], [58, 1, 1, "", "BinaryStatScores"], [50, 1, 1, "", "F1Score"], [51, 1, 1, "", "FbetaScore"], [48, 1, 1, "", "MulticlassAUROC"], [47, 1, 1, "", "MulticlassAccuracy"], [50, 1, 1, "", "MulticlassF1Score"], [51, 1, 1, "", "MulticlassFbetaScore"], [52, 1, 1, "", "MulticlassPrecision"], [53, 1, 1, "", "MulticlassPrecisionRecallCurve"], [55, 1, 1, "", "MulticlassROCCurve"], [54, 1, 1, "", "MulticlassRecall"], [56, 1, 1, "", "MulticlassSensitivity"], [57, 1, 1, "", "MulticlassSpecificity"], [58, 1, 1, "", "MulticlassStatScores"], [48, 1, 1, "", "MultilabelAUROC"], [47, 1, 1, "", "MultilabelAccuracy"], [50, 1, 1, "", "MultilabelF1Score"], [51, 1, 1, "", "MultilabelFbetaScore"], [52, 1, 1, "", "MultilabelPrecision"], [53, 1, 1, "", "MultilabelPrecisionRecallCurve"], [55, 1, 1, "", "MultilabelROCCurve"], [54, 1, 1, "", "MultilabelRecall"], [56, 1, 1, "", "MultilabelSensitivity"], [57, 1, 1, "", "MultilabelSpecificity"], [58, 1, 1, "", "MultilabelStatScores"], [52, 1, 1, "", "Precision"], [53, 1, 1, "", "PrecisionRecallCurve"], [55, 1, 1, "", "ROCCurve"], [54, 1, 1, "", "Recall"], [56, 1, 1, "", "Sensitivity"], [57, 1, 1, "", "Specificity"], [58, 1, 1, "", "StatScores"]], "cyclops.evaluate.metrics.functional.accuracy": [[47, 4, 1, "", "binary_accuracy"], [47, 4, 1, "", "multiclass_accuracy"], [47, 4, 1, "", "multilabel_accuracy"]], "cyclops.evaluate.metrics.functional.auroc": [[48, 4, 1, "", "auroc"], [48, 4, 1, "", "binary_auroc"], [48, 4, 1, "", "multiclass_auroc"], [48, 4, 1, "", "multilabel_auroc"]], "cyclops.evaluate.metrics.functional.average_precision": [[49, 4, 1, "", "average_precision"], [49, 4, 1, "", "binary_average_precision"]], "cyclops.evaluate.metrics.functional.f_beta": [[50, 4, 1, "", "binary_f1_score"], [51, 4, 1, "", "binary_fbeta_score"], [50, 4, 1, "", "f1_score"], [51, 4, 1, "", "fbeta_score"], [50, 4, 1, "", "multiclass_f1_score"], [51, 4, 1, "", "multiclass_fbeta_score"], [50, 4, 1, "", "multilabel_f1_score"], [51, 4, 1, "", "multilabel_fbeta_score"]], "cyclops.evaluate.metrics.functional.precision_recall": [[52, 4, 1, "", "binary_precision"], [52, 4, 1, "", "multiclass_precision"], [54, 4, 1, "", "multiclass_recall"], [52, 4, 1, "", "multilabel_precision"], [54, 4, 1, "", "multilabel_recall"], [52, 4, 1, "", "precision"], [54, 4, 1, "", "recall"]], "cyclops.evaluate.metrics.functional.precision_recall_curve": [[53, 4, 1, "", "multiclass_precision_recall_curve"], [53, 4, 1, "", "multilabel_precision_recall_curve"], [53, 4, 1, "", "precision_recall_curve"]], "cyclops.evaluate.metrics.functional.roc": [[55, 4, 1, "", "binary_roc_curve"], [55, 4, 1, "", "multiclass_roc_curve"], [55, 4, 1, "", "multilabel_roc_curve"], [55, 4, 1, "", "roc_curve"]], "cyclops.evaluate.metrics.functional.sensitivity": [[56, 4, 1, "", "binary_sensitivity"], [56, 4, 1, "", "multiclass_sensitivity"], [56, 4, 1, "", "multilabel_sensitivity"], [56, 4, 1, "", "sensitivity"]], "cyclops.evaluate.metrics.functional.specificity": [[57, 4, 1, "", "binary_specificity"], [57, 4, 1, "", "multiclass_specificity"], [57, 4, 1, "", "multilabel_specificity"], [57, 4, 1, "", "specificity"]], "cyclops.evaluate.metrics.functional.stat_scores": [[58, 4, 1, "", "binary_stat_scores"], [58, 4, 1, "", "multiclass_stat_scores"], [58, 4, 1, "", "multilabel_stat_scores"], [58, 4, 1, "", "stat_scores"]], "cyclops.monitor": [[26, 0, 0, "-", "clinical_applicator"], [28, 0, 0, "-", "synthetic_applicator"]], "cyclops.monitor.clinical_applicator": [[27, 1, 1, "", "ClinicalShiftApplicator"]], "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator": [[27, 2, 1, "", "age"], [27, 2, 1, "", "apply_shift"], [27, 2, 1, "", "custom"], [27, 2, 1, "", "hospital_type"], [27, 2, 1, "", "month"], [27, 2, 1, "", "sex"], [27, 2, 1, "", "time"]], "cyclops.monitor.synthetic_applicator": [[29, 1, 1, "", "SyntheticShiftApplicator"], [30, 4, 1, "", "binary_noise_shift"], [31, 4, 1, "", "feature_association_shift"], [32, 4, 1, "", "feature_swap_shift"], [33, 4, 1, "", "gaussian_noise_shift"], [34, 4, 1, "", "knockout_shift"]], "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator": [[29, 2, 1, "", "apply_shift"]], "cyclops.report": [[35, 0, 0, "-", "report"]], "cyclops.report.report": [[36, 1, 1, "", "ModelCardReport"]], "cyclops.report.report.ModelCardReport": [[36, 2, 1, "", "export"], [36, 2, 1, "", "from_json_file"], [36, 2, 1, "", "log_citation"], [36, 2, 1, "", "log_dataset"], [36, 2, 1, "", "log_descriptor"], [36, 2, 1, "", "log_fairness_assessment"], [36, 2, 1, "", "log_from_dict"], [36, 2, 1, "", "log_image"], [36, 2, 1, "", "log_license"], [36, 2, 1, "", "log_model_parameters"], [36, 2, 1, "", "log_owner"], [36, 2, 1, "", "log_performance_metrics"], [36, 2, 1, "", "log_plotly_figure"], [36, 2, 1, "", "log_quantitative_analysis"], [36, 2, 1, "", "log_reference"], [36, 2, 1, "", "log_regulation"], [36, 2, 1, "", "log_risk"], [36, 2, 1, "", "log_use_case"], [36, 2, 1, "", "log_user"], [36, 2, 1, "", "log_version"]], "cyclops.tasks": [[37, 0, 0, "-", "classification"]], "cyclops.tasks.classification": [[38, 1, 1, "", "BinaryTabularClassificationTask"], [39, 1, 1, "", "MultilabelImageClassificationTask"]], "cyclops.tasks.classification.BinaryTabularClassificationTask": [[38, 2, 1, "", "__init__"], [38, 2, 1, "", "add_model"], [38, 5, 1, "", "data_type"], [38, 2, 1, "", "evaluate"], [38, 2, 1, "", "get_model"], [38, 2, 1, "", "list_models"], [38, 2, 1, "", "list_models_params"], [38, 2, 1, "", "load_model"], [38, 5, 1, "", "models_count"], [38, 2, 1, "", "predict"], [38, 2, 1, "", "save_model"], [38, 5, 1, "", "task_type"], [38, 2, 1, "", "train"]], "cyclops.tasks.classification.MultilabelImageClassificationTask": [[39, 2, 1, "", "__init__"], [39, 2, 1, "", "add_model"], [39, 5, 1, "", "data_type"], [39, 2, 1, "", "evaluate"], [39, 2, 1, "", "get_model"], [39, 2, 1, "", "list_models"], [39, 2, 1, "", "list_models_params"], [39, 2, 1, "", "load_model"], [39, 5, 1, "", "models_count"], [39, 2, 1, "", "predict"], [39, 2, 1, "", "save_model"], [39, 5, 1, "", "task_type"]]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:method", "3": "py:attribute", "4": "py:function", "5": "py:property"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "method", "Python method"], "3": ["py", "attribute", "Python attribute"], "4": ["py", "function", "Python function"], "5": ["py", "property", "Python property"]}, "titleterms": {"api": [0, 66], "refer": 0, "contribut": [1, 2], "cyclop": [1, 6, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43], "pre": 1, "commit": 1, "hook": 1, "code": 1, "guidelin": 1, "develop": 2, "us": [2, 4, 7, 64, 67], "poetri": 2, "evalu": [3, 4, 5, 44, 45, 46, 60, 61, 62, 65], "kei": 3, "capabl": 3, "breast": 4, "cancer": 4, "classif": [4, 37, 38, 39, 63, 67], "featur": [4, 14, 15, 40, 60, 61, 62, 65], "individu": 4, "metric": [4, 5, 46, 63], "metricdict": 4, "data": [4, 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 40, 60, 61, 62, 65, 67], "slice": [4, 5], "intersect": 4, "prepar": 4, "result": [4, 5], "fair": [4, 8, 45], "report": [5, 8, 35, 36, 42, 60, 61, 62, 63, 65], "gener": [5, 60, 61, 62, 63, 64, 65], "heart": [5, 61, 67], "failur": [5, 61, 67], "predict": [5, 60, 61, 62, 65, 67], "creat": [5, 60, 61, 62, 65], "model": [5, 8, 60, 61, 62, 63, 64, 65], "card": [5, 63], "load": [5, 60, 61, 63, 64], "ad": 5, "figur": 5, "ag": [5, 60, 61, 62, 63, 65], "distribut": [5, 60, 61, 62, 65], "outcom": [5, 60, 61, 62, 65], "preprocess": [5, 62, 65], "huggingfac": 5, "dataset": [5, 8, 60, 61, 62, 63, 64, 65], "train": [5, 60, 61, 62, 64, 65], "test": [5, 63, 64], "log": [5, 63], "modelcard": 5, "consider": [5, 8], "export": 5, "what": 6, "i": 6, "quick": 6, "start": 6, "instal": [7, 60], "pip": 7, "overview": [8, 9], "quantit": 8, "analysi": 8, "detail": 8, "paramet": 8, "monitor": [9, 26, 27, 28, 29, 30, 31, 32, 33, 34, 41, 66], "perform": [9, 63], "subgroup": 9, "aggreg": [10, 11, 12, 13], "tabular_as_aggreg": 12, "timestamp_ffill_agg": 13, "medical_imag": [14, 15], "medicalimag": 15, "slicer": [16, 17, 18, 19, 20, 21, 22, 23, 24, 25], "slicespec": 17, "compound_filt": 18, "filter_datetim": 19, "filter_non_nul": 20, "filter_rang": 21, "filter_string_contain": 22, "filter_valu": 23, "is_datetim": 24, "overal": 25, "clinical_appl": [26, 27], "clinicalshiftappl": 27, "synthetic_appl": [28, 29, 30, 31, 32, 33, 34], "syntheticshiftappl": 29, "binary_noise_shift": 30, "feature_association_shift": 31, "feature_swap_shift": 32, "gaussian_noise_shift": 33, "knockout_shift": 34, "modelcardreport": 36, "task": [37, 38, 39, 43, 60, 61, 62, 65], "binarytabularclassificationtask": 38, "multilabelimageclassificationtask": 39, "accuraci": 47, "modul": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "interfac": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "binaryaccuraci": 47, "multiclassaccuraci": 47, "multilabelaccuraci": 47, "function": [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], "binary_accuraci": 47, "multiclass_accuraci": 47, "multilabel_accuraci": 47, "auroc": [48, 63], "binaryauroc": 48, "multiclassauroc": 48, "multilabelauroc": 48, "binary_auroc": 48, "multiclass_auroc": 48, "multilabel_auroc": 48, "averag": 49, "precis": [49, 52], "binaryaverageprecis": 49, "average_precis": 49, "binary_average_precis": 49, "f1": 50, "score": [50, 51], "f1score": 50, "binaryf1scor": 50, "multiclassf1scor": 50, "multilabelf1scor": 50, "f1_score": 50, "binary_f1_scor": 50, "multiclass_f1_scor": 50, "multilabel_f1_scor": 50, "f": 51, "beta": 51, "fbetascor": 51, "binaryfbetascor": 51, "multiclassfbetascor": 51, "multilabelfbetascor": 51, "fbeta_scor": 51, "binary_fbeta_scor": 51, "multiclass_fbeta_scor": 51, "multilabel_fbeta_scor": 51, "binaryprecis": 52, "multiclassprecis": 52, "multilabelprecis": 52, "binary_precis": 52, "multiclass_precis": 52, "multilabel_precis": 52, "precisionrecallcurv": 53, "binaryprecisionrecallcurv": 53, "multiclassprecisionrecallcurv": 53, "multilabelprecisionrecallcurv": 53, "precision_recall_curv": 53, "multiclass_precision_recall_curv": 53, "multilabel_precision_recall_curv": 53, "recal": 54, "binaryrecal": 54, "multiclassrecal": 54, "multilabelrecal": 54, "multiclass_recal": 54, "multilabel_recal": 54, "roc": 55, "curv": 55, "roccurv": 55, "binaryroccurv": 55, "multiclassroccurv": 55, "multilabelroccurv": 55, "roc_curv": 55, "binary_roc_curv": 55, "multiclass_roc_curv": 55, "multilabel_roc_curv": 55, "sensit": [56, 64], "binarysensit": 56, "multiclasssensit": 56, "multilabelsensit": 56, "binary_sensit": 56, "multiclass_sensit": 56, "multilabel_sensit": 56, "specif": 57, "binaryspecif": 57, "multiclassspecif": 57, "multilabelspecif": 57, "binary_specif": 57, "multiclass_specif": 57, "multilabel_specif": 57, "statscor": 58, "binarystatscor": 58, "multiclassstatscor": 58, "multilabelstatscor": 58, "stat_scor": 58, "binary_stat_scor": 58, "multiclass_stat_scor": 58, "multilabel_stat_scor": 58, "tutori": [59, 64], "readmiss": [60, 67], "librari": [60, 61, 62, 63, 64, 65], "import": [60, 61, 62, 63, 64, 65], "constant": [60, 61, 62, 65], "sex": [60, 61, 63], "valu": [60, 61], "identifi": [60, 61, 62, 65], "type": [60, 61, 62, 65], "preprocessor": [60, 61, 62, 65], "hug": [60, 61, 62, 65], "face": [60, 61, 62, 65], "creation": [60, 61, 62, 63, 65], "mortal": [62, 67], "queri": [62, 65], "process": 62, "comput": [62, 65], "label": [62, 65], "inspect": [62, 65], "drop": [62, 65], "nan": [62, 65], "base": [62, 65], "nan_threshold": [62, 65], "gender": [62, 65], "chest": [63, 67], "x": [63, 67], "rai": [63, 67], "diseas": 63, "histor": 63, "initi": 63, "period": 63, "multilabel": 63, "pathologi": 63, "w": 63, "threshold": 63, "popul": 63, "field": 63, "nihcxr": 64, "clinic": 64, "drift": 64, "experi": 64, "exampl": [64, 67], "1": 64, "sourc": 64, "target": 64, "2": 64, "3": 64, "dimension": 64, "reduct": 64, "techniqu": 64, "differ": 64, "4": 64, "shift": 64, "5": 64, "roll": 64, "window": 64, "synthet": 64, "timestamp": 64, "biweekli": 64, "prolong": [65, 67], "length": [65, 67], "stai": [65, 67], "case": 67, "tabular": 67, "kaggl": 67, "mimiciv": 67, "synthea": 67, "diabet": 67, "130": 67, "u": 67, "hospit": 67, "year": 67, "1999": 67, "2008": 67, "imag": 67, "nih": 67, "user": 68, "guid": 68}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.todo": 2, "sphinx.ext.viewcode": 1, "sphinx.ext.intersphinx": 1, "nbsphinx": 4, "sphinx": 60}, "alltitles": {"API Reference": [[0, "api-reference"]], "Contributing to cyclops": [[1, "contributing-to-cyclops"]], "Pre-commit hooks": [[1, "pre-commit-hooks"]], "Coding guidelines": [[1, "coding-guidelines"]], "\ud83e\uddd1\ud83c\udfff\u200d\ud83d\udcbb Developing": [[2, "developing"]], "Using poetry": [[2, "using-poetry"]], "Contributing": [[2, "contributing"]], "Evaluation": [[3, "evaluation"], [5, "Evaluation"], [60, "Evaluation"], [61, "Evaluation"], [62, "Evaluation"], [65, "Evaluation"]], "Key capabilities": [[3, "key-capabilities"]], "Breast Cancer Classification and Evaluation": [[4, "Breast-Cancer-Classification-and-Evaluation"]], "Features": [[4, "Features"]], "Individual Metrics": [[4, "Individual-Metrics"]], "Using MetricDict": [[4, "Using-MetricDict"]], "Data Slicing": [[4, "Data-Slicing"], [5, "Data-Slicing"]], "Intersectional slicing": [[4, "Intersectional-slicing"]], "Preparing Result": [[4, "Preparing-Result"]], "Fairness Evaluator": [[4, "Fairness-Evaluator"], [45, "fairness-evaluator"]], "Report Generation for Heart Failure Prediction": [[5, "Report-Generation-for-Heart-Failure-Prediction"]], "Create Model Card Report": [[5, "Create-Model-Card-Report"]], "Data Loading": [[5, "Data-Loading"], [60, "Data-Loading"], [61, "Data-Loading"]], "Adding figures to report": [[5, "Adding-figures-to-report"]], "Age distribution figure": [[5, "Age-distribution-figure"]], "Outcome distribution": [[5, "Outcome-distribution"], [60, "Outcome-distribution"], [61, "Outcome-distribution"], [62, "Outcome-distribution"], [65, "Outcome-distribution"]], "Data Preprocessing": [[5, "Data-Preprocessing"]], "Create HuggingFace Dataset": [[5, "Create-HuggingFace-Dataset"]], "Training": [[5, "Training"], [60, "Training"], [61, "Training"], [62, "Training"], [65, "Training"]], "Age distribution in test data": [[5, "Age-distribution-in-test-data"]], "Logging metrics and results to report": [[5, "Logging-metrics-and-results-to-report"]], "Report Generation": [[5, "Report-Generation"], [60, "Report-Generation"], [61, "Report-Generation"], [62, "Report-Generation"], [65, "Report-Generation"]], "ModelCard": [[5, "ModelCard"]], "Considerations": [[5, "Considerations"], [8, "considerations"]], "Exporting report": [[5, "Exporting-report"]], "What is CyclOps?": [[6, "what-is-cyclops"]], "Quick Start": [[6, "quick-start"]], "Installation": [[7, "installation"]], "Using pip": [[7, "using-pip"]], "Model Report": [[8, "model-report"]], "Overview": [[8, "overview"]], "Dataset": [[8, "dataset"]], "Quantitative Analysis": [[8, "quantitative-analysis"]], "Fairness Analysis": [[8, "fairness-analysis"]], "Model Details": [[8, "model-details"]], "Model Parameters": [[8, "model-parameters"]], "Monitoring": [[9, "monitoring"]], "Overview Performance": [[9, "overview-performance"]], "Subgroup Performance": [[9, "subgroup-performance"]], "cyclops.data.aggregate": [[10, "module-cyclops.data.aggregate"]], "cyclops.data.aggregate.Aggregator": [[11, "cyclops-data-aggregate-aggregator"]], "cyclops.data.aggregate.tabular_as_aggregated": [[12, "cyclops-data-aggregate-tabular-as-aggregated"]], "cyclops.data.aggregate.timestamp_ffill_agg": [[13, "cyclops-data-aggregate-timestamp-ffill-agg"]], "cyclops.data.features.medical_image": [[14, "module-cyclops.data.features.medical_image"]], "cyclops.data.features.medical_image.MedicalImage": [[15, "cyclops-data-features-medical-image-medicalimage"]], "cyclops.data.slicer": [[16, "module-cyclops.data.slicer"]], "cyclops.data.slicer.SliceSpec": [[17, "cyclops-data-slicer-slicespec"]], "cyclops.data.slicer.compound_filter": [[18, "cyclops-data-slicer-compound-filter"]], "cyclops.data.slicer.filter_datetime": [[19, "cyclops-data-slicer-filter-datetime"]], "cyclops.data.slicer.filter_non_null": [[20, "cyclops-data-slicer-filter-non-null"]], "cyclops.data.slicer.filter_range": [[21, "cyclops-data-slicer-filter-range"]], "cyclops.data.slicer.filter_string_contains": [[22, "cyclops-data-slicer-filter-string-contains"]], "cyclops.data.slicer.filter_value": [[23, "cyclops-data-slicer-filter-value"]], "cyclops.data.slicer.is_datetime": [[24, "cyclops-data-slicer-is-datetime"]], "cyclops.data.slicer.overall": [[25, "cyclops-data-slicer-overall"]], "cyclops.monitor.clinical_applicator": [[26, "module-cyclops.monitor.clinical_applicator"]], "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator": [[27, "cyclops-monitor-clinical-applicator-clinicalshiftapplicator"]], "cyclops.monitor.synthetic_applicator": [[28, "module-cyclops.monitor.synthetic_applicator"]], "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator": [[29, "cyclops-monitor-synthetic-applicator-syntheticshiftapplicator"]], "cyclops.monitor.synthetic_applicator.binary_noise_shift": [[30, "cyclops-monitor-synthetic-applicator-binary-noise-shift"]], "cyclops.monitor.synthetic_applicator.feature_association_shift": [[31, "cyclops-monitor-synthetic-applicator-feature-association-shift"]], "cyclops.monitor.synthetic_applicator.feature_swap_shift": [[32, "cyclops-monitor-synthetic-applicator-feature-swap-shift"]], "cyclops.monitor.synthetic_applicator.gaussian_noise_shift": [[33, "cyclops-monitor-synthetic-applicator-gaussian-noise-shift"]], "cyclops.monitor.synthetic_applicator.knockout_shift": [[34, "cyclops-monitor-synthetic-applicator-knockout-shift"]], "cyclops.report.report": [[35, "module-cyclops.report.report"]], "cyclops.report.report.ModelCardReport": [[36, "cyclops-report-report-modelcardreport"]], "cyclops.tasks.classification": [[37, "module-cyclops.tasks.classification"]], "cyclops.tasks.classification.BinaryTabularClassificationTask": [[38, "cyclops-tasks-classification-binarytabularclassificationtask"]], "cyclops.tasks.classification.MultilabelImageClassificationTask": [[39, "cyclops-tasks-classification-multilabelimageclassificationtask"]], "cyclops.data": [[40, "module-cyclops.data"]], "cyclops.data.features": [[40, "module-cyclops.data.features"]], "cyclops.monitor": [[41, "module-cyclops.monitor"]], "cyclops.report": [[42, "module-cyclops.report"]], "cyclops.tasks": [[43, "module-cyclops.tasks"]], "Evaluator": [[44, "evaluator"]], "Evaluation Metrics": [[46, "evaluation-metrics"]], "Accuracy": [[47, "accuracy"], [47, "id1"]], "Module Interface": [[47, "module-interface"], [48, "module-interface"], [49, "module-interface"], [50, "module-interface"], [51, "module-interface"], [52, "module-interface"], [53, "module-interface"], [54, "module-interface"], [55, "module-interface"], [56, "module-interface"], [57, "module-interface"], [58, "module-interface"]], "BinaryAccuracy": [[47, "binaryaccuracy"]], "MulticlassAccuracy": [[47, "multiclassaccuracy"]], "MultilabelAccuracy": [[47, "multilabelaccuracy"]], "Functional Interface": [[47, "functional-interface"], [48, "functional-interface"], [49, "functional-interface"], [50, "functional-interface"], [51, "functional-interface"], [52, "functional-interface"], [53, "functional-interface"], [54, "functional-interface"], [55, "functional-interface"], [56, "functional-interface"], [57, "functional-interface"], [58, "functional-interface"]], "binary_accuracy": [[47, "binary-accuracy"]], "multiclass_accuracy": [[47, "multiclass-accuracy"]], "multilabel_accuracy": [[47, "multilabel-accuracy"]], "AUROC": [[48, "auroc"], [48, "id1"]], "BinaryAUROC": [[48, "binaryauroc"]], "MulticlassAUROC": [[48, "multiclassauroc"]], "MultilabelAUROC": [[48, "multilabelauroc"]], "auroc": [[48, "id2"]], "binary_auroc": [[48, "binary-auroc"]], "multiclass_auroc": [[48, "multiclass-auroc"]], "multilabel_auroc": [[48, "multilabel-auroc"]], "Average Precision": [[49, "average-precision"]], "BinaryAveragePrecision": [[49, "binaryaverageprecision"]], "average_precision": [[49, "id1"]], "binary_average_precision": [[49, "binary-average-precision"]], "F1-Score": [[50, "f1-score"]], "F1Score": [[50, "f1score"]], "BinaryF1Score": [[50, "binaryf1score"]], "MulticlassF1Score": [[50, "multiclassf1score"]], "MultilabelF1Score": [[50, "multilabelf1score"]], "f1_score": [[50, "id1"]], "binary_f1_score": [[50, "binary-f1-score"]], "multiclass_f1_score": [[50, "multiclass-f1-score"]], "multilabel_f1_score": [[50, "multilabel-f1-score"]], "F-beta Score": [[51, "f-beta-score"]], "FbetaScore": [[51, "fbetascore"]], "BinaryFbetaScore": [[51, "binaryfbetascore"]], "MulticlassFbetaScore": [[51, "multiclassfbetascore"]], "MultilabelFbetaScore": [[51, "multilabelfbetascore"]], "fbeta_score": [[51, "fbeta-score"]], "binary_fbeta_score": [[51, "binary-fbeta-score"]], "multiclass_fbeta_score": [[51, "multiclass-fbeta-score"]], "multilabel_fbeta_score": [[51, "multilabel-fbeta-score"]], "Precision": [[52, "precision"], [52, "id1"]], "BinaryPrecision": [[52, "binaryprecision"]], "MulticlassPrecision": [[52, "multiclassprecision"]], "MultilabelPrecision": [[52, "multilabelprecision"]], "precision": [[52, "id2"]], "binary_precision": [[52, "binary-precision"]], "multiclass_precision": [[52, "multiclass-precision"]], "multilabel_precision": [[52, "multilabel-precision"]], "PrecisionRecallCurve": [[53, "precisionrecallcurve"], [53, "id1"]], "BinaryPrecisionRecallCurve": [[53, "binaryprecisionrecallcurve"]], "MulticlassPrecisionRecallCurve": [[53, "multiclassprecisionrecallcurve"]], "MultilabelPrecisionRecallCurve": [[53, "multilabelprecisionrecallcurve"]], "precision_recall_curve": [[53, "precision-recall-curve"]], "multiclass_precision_recall_curve": [[53, "multiclass-precision-recall-curve"]], "multilabel_precision_recall_curve": [[53, "multilabel-precision-recall-curve"]], "Recall": [[54, "recall"], [54, "id1"]], "BinaryRecall": [[54, "binaryrecall"]], "MulticlassRecall": [[54, "multiclassrecall"]], "MultilabelRecall": [[54, "multilabelrecall"]], "recall": [[54, "id2"]], "multiclass_recall": [[54, "multiclass-recall"]], "multilabel_recall": [[54, "multilabel-recall"]], "ROC Curve": [[55, "roc-curve"]], "ROCCurve": [[55, "roccurve"]], "BinaryROCCurve": [[55, "binaryroccurve"]], "MulticlassROCCurve": [[55, "multiclassroccurve"]], "MultilabelROCCurve": [[55, "multilabelroccurve"]], "roc_curve": [[55, "id1"]], "binary_roc_curve": [[55, "binary-roc-curve"]], "multiclass_roc_curve": [[55, "multiclass-roc-curve"]], "multilabel_roc_curve": [[55, "multilabel-roc-curve"]], "Sensitivity": [[56, "sensitivity"], [56, "id1"]], "BinarySensitivity": [[56, "binarysensitivity"]], "MulticlassSensitivity": [[56, "multiclasssensitivity"]], "MultilabelSensitivity": [[56, "multilabelsensitivity"]], "sensitivity": [[56, "id2"]], "binary_sensitivity": [[56, "binary-sensitivity"]], "multiclass_sensitivity": [[56, "multiclass-sensitivity"]], "multilabel_sensitivity": [[56, "multilabel-sensitivity"]], "Specificity": [[57, "specificity"], [57, "id1"]], "BinarySpecificity": [[57, "binaryspecificity"]], "MulticlassSpecificity": [[57, "multiclassspecificity"]], "MultilabelSpecificity": [[57, "multilabelspecificity"]], "specificity": [[57, "id2"]], "binary_specificity": [[57, "binary-specificity"]], "multiclass_specificity": [[57, "multiclass-specificity"]], "multilabel_specificity": [[57, "multilabel-specificity"]], "StatScores": [[58, "statscores"], [58, "id1"]], "BinaryStatScores": [[58, "binarystatscores"]], "MulticlassStatScores": [[58, "multiclassstatscores"]], "MultilabelStatScores": [[58, "multilabelstatscores"]], "stat_scores": [[58, "stat-scores"]], "binary_stat_scores": [[58, "binary-stat-scores"]], "multiclass_stat_scores": [[58, "multiclass-stat-scores"]], "multilabel_stat_scores": [[58, "multilabel-stat-scores"]], "Tutorials": [[59, "tutorials"]], "Readmission Prediction": [[60, "Readmission-Prediction"]], "Install libraries": [[60, "Install-libraries"]], "Import Libraries": [[60, "Import-Libraries"], [61, "Import-Libraries"], [62, "Import-Libraries"], [63, "Import-Libraries"], [65, "Import-Libraries"]], "Constants": [[60, "Constants"], [61, "Constants"], [62, "Constants"], [65, "Constants"]], "Sex values": [[60, "Sex-values"], [61, "Sex-values"]], "Age distribution": [[60, "Age-distribution"], [61, "Age-distribution"], [62, "Age-distribution"], [65, "Age-distribution"]], "Identifying feature types": [[60, "Identifying-feature-types"], [61, "Identifying-feature-types"], [62, "Identifying-feature-types"], [65, "Identifying-feature-types"]], "Creating data preprocessors": [[60, "Creating-data-preprocessors"], [61, "Creating-data-preprocessors"], [62, "Creating-data-preprocessors"], [65, "Creating-data-preprocessors"]], "Creating Hugging Face Dataset": [[60, "Creating-Hugging-Face-Dataset"], [61, "Creating-Hugging-Face-Dataset"], [62, "Creating-Hugging-Face-Dataset"], [65, "Creating-Hugging-Face-Dataset"]], "Model Creation": [[60, "Model-Creation"], [61, "Model-Creation"], [62, "Model-Creation"], [63, "Model-Creation"], [65, "Model-Creation"]], "Task Creation": [[60, "Task-Creation"], [61, "Task-Creation"], [62, "Task-Creation"], [65, "Task-Creation"]], "Prediction": [[60, "Prediction"], [61, "Prediction"], [62, "Prediction"], [65, "Prediction"]], "Heart Failure Prediction": [[61, "Heart-Failure-Prediction"]], "Mortality Prediction": [[62, "Mortality-Prediction"]], "Data Querying & Processing": [[62, "Data-Querying-&-Processing"]], "Compute mortality (labels)": [[62, "Compute-mortality-(labels)"]], "Data Inspection and Preprocessing": [[62, "Data-Inspection-and-Preprocessing"], [65, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[62, "Drop-NaNs-based-on-the-NAN_THRESHOLD"], [65, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Gender distribution": [[62, "Gender-distribution"], [65, "Gender-distribution"]], "Chest X-Ray Disease Classification": [[63, "Chest-X-Ray-Disease-Classification"]], "Generate Historical Reports": [[63, "Generate-Historical-Reports"]], "Initialize Periodic Report": [[63, "Initialize-Periodic-Report"]], "Load Dataset": [[63, "Load-Dataset"]], "Multilabel AUROC by Pathology and Sex": [[63, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[63, "Multilabel-AUROC-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[63, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[63, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[64, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[64, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[64, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[64, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[64, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[64, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[64, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[65, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[65, "Data-Querying"]], "Compute length of stay (labels)": [[65, "Compute-length-of-stay-(labels)"]], "Length of stay distribution": [[65, "Length-of-stay-distribution"]], "monitor API": [[66, "monitor-api"]], "Example use cases": [[67, "example-use-cases"]], "Tabular data": [[67, "tabular-data"]], "Kaggle Heart Failure Prediction": [[67, "kaggle-heart-failure-prediction"]], "MIMICIV Mortality Prediction": [[67, "mimiciv-mortality-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[67, "synthea-prolonged-length-of-stay-prediction"]], "Diabetes 130-US Hospitals for Years 1999-2008 Readmission Prediction": [[67, "diabetes-130-us-hospitals-for-years-1999-2008-readmission-prediction"]], "Image data": [[67, "image-data"]], "NIH Chest X-ray classification": [[67, "nih-chest-x-ray-classification"]], "User Guide": [[68, "user-guide"]]}, "indexentries": {"cyclops.data.aggregate": [[10, "module-cyclops.data.aggregate"]], "module": [[10, "module-cyclops.data.aggregate"], [14, "module-cyclops.data.features.medical_image"], [16, "module-cyclops.data.slicer"], [26, "module-cyclops.monitor.clinical_applicator"], [28, "module-cyclops.monitor.synthetic_applicator"], [35, "module-cyclops.report.report"], [37, "module-cyclops.tasks.classification"], [40, "module-cyclops.data"], [40, "module-cyclops.data.features"], [41, "module-cyclops.monitor"], [42, "module-cyclops.report"], [43, "module-cyclops.tasks"]], "aggregator (class in cyclops.data.aggregate)": [[11, "cyclops.data.aggregate.Aggregator"]], "__init__() (aggregator method)": [[11, "cyclops.data.aggregate.Aggregator.__init__"]], "agg_by (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.agg_by"]], "agg_meta_for (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.agg_meta_for"]], "aggfuncs (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.aggfuncs"]], "fit() (aggregator method)": [[11, "cyclops.data.aggregate.Aggregator.fit"]], "fit_transform() (aggregator method)": [[11, "cyclops.data.aggregate.Aggregator.fit_transform"]], "imputer (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.imputer"]], "num_timesteps (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.num_timesteps"]], "set_output() (aggregator method)": [[11, "cyclops.data.aggregate.Aggregator.set_output"]], "time_by (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.time_by"]], "timestamp_col (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.timestamp_col"]], "timestep_size (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.timestep_size"]], "transform() (aggregator method)": [[11, "cyclops.data.aggregate.Aggregator.transform"]], "window_duration (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.window_duration"]], "window_start_time (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.window_start_time"]], "window_stop_time (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.window_stop_time"]], "window_times (aggregator attribute)": [[11, "cyclops.data.aggregate.Aggregator.window_times"]], "tabular_as_aggregated() (in module cyclops.data.aggregate)": [[12, "cyclops.data.aggregate.tabular_as_aggregated"]], "timestamp_ffill_agg() (in module cyclops.data.aggregate)": [[13, "cyclops.data.aggregate.timestamp_ffill_agg"]], "cyclops.data.features.medical_image": [[14, "module-cyclops.data.features.medical_image"]], "medicalimage (class in cyclops.data.features.medical_image)": [[15, "cyclops.data.features.medical_image.MedicalImage"]], "__call__() (medicalimage method)": [[15, "cyclops.data.features.medical_image.MedicalImage.__call__"]], "cast_storage() (medicalimage method)": [[15, "cyclops.data.features.medical_image.MedicalImage.cast_storage"]], "decode_example() (medicalimage method)": [[15, "cyclops.data.features.medical_image.MedicalImage.decode_example"]], "embed_storage() (medicalimage method)": [[15, "cyclops.data.features.medical_image.MedicalImage.embed_storage"]], "encode_example() (medicalimage method)": [[15, "cyclops.data.features.medical_image.MedicalImage.encode_example"]], "flatten() (medicalimage method)": [[15, "cyclops.data.features.medical_image.MedicalImage.flatten"]], "cyclops.data.slicer": [[16, "module-cyclops.data.slicer"]], "slicespec (class in cyclops.data.slicer)": [[17, "cyclops.data.slicer.SliceSpec"]], "_registry (slicespec attribute)": [[17, "cyclops.data.slicer.SliceSpec._registry"]], "add_slice_spec() (slicespec method)": [[17, "cyclops.data.slicer.SliceSpec.add_slice_spec"]], "column_names (slicespec attribute)": [[17, "cyclops.data.slicer.SliceSpec.column_names"]], "get_slices() (slicespec method)": [[17, "cyclops.data.slicer.SliceSpec.get_slices"]], "include_overall (slicespec attribute)": [[17, "cyclops.data.slicer.SliceSpec.include_overall"]], "slices() (slicespec method)": [[17, "cyclops.data.slicer.SliceSpec.slices"]], "spec_list (slicespec attribute)": [[17, "cyclops.data.slicer.SliceSpec.spec_list"]], "validate (slicespec attribute)": [[17, "cyclops.data.slicer.SliceSpec.validate"]], "compound_filter() (in module cyclops.data.slicer)": [[18, "cyclops.data.slicer.compound_filter"]], "filter_datetime() (in module cyclops.data.slicer)": [[19, "cyclops.data.slicer.filter_datetime"]], "filter_non_null() (in module cyclops.data.slicer)": [[20, "cyclops.data.slicer.filter_non_null"]], "filter_range() (in module cyclops.data.slicer)": [[21, "cyclops.data.slicer.filter_range"]], "filter_string_contains() (in module cyclops.data.slicer)": [[22, "cyclops.data.slicer.filter_string_contains"]], "filter_value() (in module cyclops.data.slicer)": [[23, "cyclops.data.slicer.filter_value"]], "is_datetime() (in module cyclops.data.slicer)": [[24, "cyclops.data.slicer.is_datetime"]], "overall() (in module cyclops.data.slicer)": [[25, "cyclops.data.slicer.overall"]], "cyclops.monitor.clinical_applicator": [[26, "module-cyclops.monitor.clinical_applicator"]], "clinicalshiftapplicator (class in cyclops.monitor.clinical_applicator)": [[27, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator"]], "age() (clinicalshiftapplicator method)": [[27, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.age"]], "apply_shift() (clinicalshiftapplicator method)": [[27, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.apply_shift"]], "custom() (clinicalshiftapplicator method)": [[27, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.custom"]], "hospital_type() (clinicalshiftapplicator method)": [[27, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.hospital_type"]], "month() (clinicalshiftapplicator method)": [[27, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.month"]], "sex() (clinicalshiftapplicator method)": [[27, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.sex"]], "time() (clinicalshiftapplicator method)": [[27, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.time"]], "cyclops.monitor.synthetic_applicator": [[28, "module-cyclops.monitor.synthetic_applicator"]], "syntheticshiftapplicator (class in cyclops.monitor.synthetic_applicator)": [[29, "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator"]], "apply_shift() (syntheticshiftapplicator method)": [[29, "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.apply_shift"]], "binary_noise_shift() (in module cyclops.monitor.synthetic_applicator)": [[30, "cyclops.monitor.synthetic_applicator.binary_noise_shift"]], "feature_association_shift() (in module cyclops.monitor.synthetic_applicator)": [[31, "cyclops.monitor.synthetic_applicator.feature_association_shift"]], "feature_swap_shift() (in module cyclops.monitor.synthetic_applicator)": [[32, "cyclops.monitor.synthetic_applicator.feature_swap_shift"]], "gaussian_noise_shift() (in module cyclops.monitor.synthetic_applicator)": [[33, "cyclops.monitor.synthetic_applicator.gaussian_noise_shift"]], "knockout_shift() (in module cyclops.monitor.synthetic_applicator)": [[34, "cyclops.monitor.synthetic_applicator.knockout_shift"]], "cyclops.report.report": [[35, "module-cyclops.report.report"]], "modelcardreport (class in cyclops.report.report)": [[36, "cyclops.report.report.ModelCardReport"]], "export() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.export"]], "from_json_file() (modelcardreport class method)": [[36, "cyclops.report.report.ModelCardReport.from_json_file"]], "log_citation() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_citation"]], "log_dataset() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_dataset"]], "log_descriptor() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_descriptor"]], "log_fairness_assessment() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_fairness_assessment"]], "log_from_dict() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_from_dict"]], "log_image() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_image"]], "log_license() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_license"]], "log_model_parameters() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_model_parameters"]], "log_owner() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_owner"]], "log_performance_metrics() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_performance_metrics"]], "log_plotly_figure() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_plotly_figure"]], "log_quantitative_analysis() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_quantitative_analysis"]], "log_reference() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_reference"]], "log_regulation() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_regulation"]], "log_risk() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_risk"]], "log_use_case() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_use_case"]], "log_user() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_user"]], "log_version() (modelcardreport method)": [[36, "cyclops.report.report.ModelCardReport.log_version"]], "cyclops.tasks.classification": [[37, "module-cyclops.tasks.classification"]], "binarytabularclassificationtask (class in cyclops.tasks.classification)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask"]], "__init__() (binarytabularclassificationtask method)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.__init__"]], "add_model() (binarytabularclassificationtask method)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.add_model"]], "data_type (binarytabularclassificationtask property)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.data_type"]], "evaluate() (binarytabularclassificationtask method)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.evaluate"]], "get_model() (binarytabularclassificationtask method)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.get_model"]], "list_models() (binarytabularclassificationtask method)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.list_models"]], "list_models_params() (binarytabularclassificationtask method)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.list_models_params"]], "load_model() (binarytabularclassificationtask method)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.load_model"]], "models_count (binarytabularclassificationtask property)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.models_count"]], "predict() (binarytabularclassificationtask method)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.predict"]], "save_model() (binarytabularclassificationtask method)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.save_model"]], "task_type (binarytabularclassificationtask property)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.task_type"]], "train() (binarytabularclassificationtask method)": [[38, "cyclops.tasks.classification.BinaryTabularClassificationTask.train"]], "multilabelimageclassificationtask (class in cyclops.tasks.classification)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask"]], "__init__() (multilabelimageclassificationtask method)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.__init__"]], "add_model() (multilabelimageclassificationtask method)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.add_model"]], "data_type (multilabelimageclassificationtask property)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.data_type"]], "evaluate() (multilabelimageclassificationtask method)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.evaluate"]], "get_model() (multilabelimageclassificationtask method)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.get_model"]], "list_models() (multilabelimageclassificationtask method)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.list_models"]], "list_models_params() (multilabelimageclassificationtask method)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.list_models_params"]], "load_model() (multilabelimageclassificationtask method)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.load_model"]], "models_count (multilabelimageclassificationtask property)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.models_count"]], "predict() (multilabelimageclassificationtask method)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.predict"]], "save_model() (multilabelimageclassificationtask method)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.save_model"]], "task_type (multilabelimageclassificationtask property)": [[39, "cyclops.tasks.classification.MultilabelImageClassificationTask.task_type"]], "cyclops.data": [[40, "module-cyclops.data"]], "cyclops.data.features": [[40, "module-cyclops.data.features"]], "cyclops.monitor": [[41, "module-cyclops.monitor"]], "cyclops.report": [[42, "module-cyclops.report"]], "cyclops.tasks": [[43, "module-cyclops.tasks"]], "evaluate() (in module cyclops.evaluate.evaluator)": [[44, "cyclops.evaluate.evaluator.evaluate"]], "evaluate_fairness() (in module cyclops.evaluate.fairness.evaluator)": [[45, "cyclops.evaluate.fairness.evaluator.evaluate_fairness"]], "accuracy (class in cyclops.evaluate.metrics)": [[47, "cyclops.evaluate.metrics.Accuracy"]], "binaryaccuracy (class in cyclops.evaluate.metrics)": [[47, "cyclops.evaluate.metrics.BinaryAccuracy"]], "multiclassaccuracy (class in cyclops.evaluate.metrics)": [[47, "cyclops.evaluate.metrics.MulticlassAccuracy"]], "multilabelaccuracy (class in cyclops.evaluate.metrics)": [[47, "cyclops.evaluate.metrics.MultilabelAccuracy"]], "binary_accuracy() (in module cyclops.evaluate.metrics.functional.accuracy)": [[47, "cyclops.evaluate.metrics.functional.accuracy.binary_accuracy"]], "multiclass_accuracy() (in module cyclops.evaluate.metrics.functional.accuracy)": [[47, "cyclops.evaluate.metrics.functional.accuracy.multiclass_accuracy"]], "multilabel_accuracy() (in module cyclops.evaluate.metrics.functional.accuracy)": [[47, "cyclops.evaluate.metrics.functional.accuracy.multilabel_accuracy"]], "auroc (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.AUROC"]], "binaryauroc (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.BinaryAUROC"]], "multiclassauroc (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.MulticlassAUROC"]], "multilabelauroc (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.MultilabelAUROC"]], "auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[48, "cyclops.evaluate.metrics.functional.auroc.auroc"]], "binary_auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[48, "cyclops.evaluate.metrics.functional.auroc.binary_auroc"]], "multiclass_auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[48, "cyclops.evaluate.metrics.functional.auroc.multiclass_auroc"]], "multilabel_auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[48, "cyclops.evaluate.metrics.functional.auroc.multilabel_auroc"]], "binaryaverageprecision (class in cyclops.evaluate.metrics)": [[49, "cyclops.evaluate.metrics.BinaryAveragePrecision"]], "average_precision() (in module cyclops.evaluate.metrics.functional.average_precision)": [[49, "cyclops.evaluate.metrics.functional.average_precision.average_precision"]], "binary_average_precision() (in module cyclops.evaluate.metrics.functional.average_precision)": [[49, "cyclops.evaluate.metrics.functional.average_precision.binary_average_precision"]], "binaryf1score (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.BinaryF1Score"]], "f1score (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.F1Score"]], "multiclassf1score (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.MulticlassF1Score"]], "multilabelf1score (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.MultilabelF1Score"]], "binary_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[50, "cyclops.evaluate.metrics.functional.f_beta.binary_f1_score"]], "f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[50, "cyclops.evaluate.metrics.functional.f_beta.f1_score"]], "multiclass_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[50, "cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score"]], "multilabel_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[50, "cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score"]], "binaryfbetascore (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.BinaryFbetaScore"]], "fbetascore (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.FbetaScore"]], "multiclassfbetascore (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.MulticlassFbetaScore"]], "multilabelfbetascore (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.MultilabelFbetaScore"]], "binary_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[51, "cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score"]], "fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[51, "cyclops.evaluate.metrics.functional.f_beta.fbeta_score"]], "multiclass_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[51, "cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score"]], "multilabel_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[51, "cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score"]], "binaryprecision (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.BinaryPrecision"]], "multiclassprecision (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.MulticlassPrecision"]], "multilabelprecision (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.MultilabelPrecision"]], "precision (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.Precision"]], "binary_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[52, "cyclops.evaluate.metrics.functional.precision_recall.binary_precision"]], "multiclass_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[52, "cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision"]], "multilabel_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[52, "cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision"]], "precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[52, "cyclops.evaluate.metrics.functional.precision_recall.precision"]], "binaryprecisionrecallcurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.BinaryPrecisionRecallCurve"]], "multiclassprecisionrecallcurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.MulticlassPrecisionRecallCurve"]], "multilabelprecisionrecallcurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.MultilabelPrecisionRecallCurve"]], "precisionrecallcurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.PrecisionRecallCurve"]], "multiclass_precision_recall_curve() (in module cyclops.evaluate.metrics.functional.precision_recall_curve)": [[53, "cyclops.evaluate.metrics.functional.precision_recall_curve.multiclass_precision_recall_curve"]], "multilabel_precision_recall_curve() (in module cyclops.evaluate.metrics.functional.precision_recall_curve)": [[53, "cyclops.evaluate.metrics.functional.precision_recall_curve.multilabel_precision_recall_curve"]], "precision_recall_curve() (in module cyclops.evaluate.metrics.functional.precision_recall_curve)": [[53, "cyclops.evaluate.metrics.functional.precision_recall_curve.precision_recall_curve"]], "binaryrecall (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.BinaryRecall"]], "multiclassrecall (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.MulticlassRecall"]], "multilabelrecall (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.MultilabelRecall"]], "recall (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.Recall"]], "multiclass_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[54, "cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall"]], "multilabel_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[54, "cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall"]], "recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[54, "cyclops.evaluate.metrics.functional.precision_recall.recall"]], "binaryroccurve (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.BinaryROCCurve"]], "multiclassroccurve (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.MulticlassROCCurve"]], "multilabelroccurve (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.MultilabelROCCurve"]], "roccurve (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.ROCCurve"]], "binary_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[55, "cyclops.evaluate.metrics.functional.roc.binary_roc_curve"]], "multiclass_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[55, "cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve"]], "multilabel_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[55, "cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve"]], "roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[55, "cyclops.evaluate.metrics.functional.roc.roc_curve"]], "binarysensitivity (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.BinarySensitivity"]], "multiclasssensitivity (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.MulticlassSensitivity"]], "multilabelsensitivity (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.MultilabelSensitivity"]], "sensitivity (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.Sensitivity"]], "binary_sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[56, "cyclops.evaluate.metrics.functional.sensitivity.binary_sensitivity"]], "multiclass_sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[56, "cyclops.evaluate.metrics.functional.sensitivity.multiclass_sensitivity"]], "multilabel_sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[56, "cyclops.evaluate.metrics.functional.sensitivity.multilabel_sensitivity"]], "sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[56, "cyclops.evaluate.metrics.functional.sensitivity.sensitivity"]], "binaryspecificity (class in cyclops.evaluate.metrics)": [[57, "cyclops.evaluate.metrics.BinarySpecificity"]], "multiclassspecificity (class in cyclops.evaluate.metrics)": [[57, "cyclops.evaluate.metrics.MulticlassSpecificity"]], "multilabelspecificity (class in cyclops.evaluate.metrics)": [[57, "cyclops.evaluate.metrics.MultilabelSpecificity"]], "specificity (class in cyclops.evaluate.metrics)": [[57, "cyclops.evaluate.metrics.Specificity"]], "binary_specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[57, "cyclops.evaluate.metrics.functional.specificity.binary_specificity"]], "multiclass_specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[57, "cyclops.evaluate.metrics.functional.specificity.multiclass_specificity"]], "multilabel_specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[57, "cyclops.evaluate.metrics.functional.specificity.multilabel_specificity"]], "specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[57, "cyclops.evaluate.metrics.functional.specificity.specificity"]], "binarystatscores (class in cyclops.evaluate.metrics)": [[58, "cyclops.evaluate.metrics.BinaryStatScores"]], "multiclassstatscores (class in cyclops.evaluate.metrics)": [[58, "cyclops.evaluate.metrics.MulticlassStatScores"]], "multilabelstatscores (class in cyclops.evaluate.metrics)": [[58, "cyclops.evaluate.metrics.MultilabelStatScores"]], "statscores (class in cyclops.evaluate.metrics)": [[58, "cyclops.evaluate.metrics.StatScores"]], "binary_stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[58, "cyclops.evaluate.metrics.functional.stat_scores.binary_stat_scores"]], "multiclass_stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[58, "cyclops.evaluate.metrics.functional.stat_scores.multiclass_stat_scores"]], "multilabel_stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[58, "cyclops.evaluate.metrics.functional.stat_scores.multilabel_stat_scores"]], "stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[58, "cyclops.evaluate.metrics.functional.stat_scores.stat_scores"]]}})
\ No newline at end of file
+Search.setIndex({"docnames": ["api", "contributing", "developing", "evaluation", "index", "installation", "model_report", "monitoring", "reference/api/_autosummary/cyclops.data.aggregate", "reference/api/_autosummary/cyclops.data.aggregate.Aggregator", "reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated", "reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg", "reference/api/_autosummary/cyclops.data.features.medical_image", "reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage", "reference/api/_autosummary/cyclops.data.slicer", "reference/api/_autosummary/cyclops.data.slicer.SliceSpec", "reference/api/_autosummary/cyclops.data.slicer.compound_filter", "reference/api/_autosummary/cyclops.data.slicer.filter_datetime", "reference/api/_autosummary/cyclops.data.slicer.filter_non_null", "reference/api/_autosummary/cyclops.data.slicer.filter_range", "reference/api/_autosummary/cyclops.data.slicer.filter_string_contains", "reference/api/_autosummary/cyclops.data.slicer.filter_value", "reference/api/_autosummary/cyclops.data.slicer.is_datetime", "reference/api/_autosummary/cyclops.data.slicer.overall", "reference/api/_autosummary/cyclops.monitor.clinical_applicator", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift", "reference/api/_autosummary/cyclops.report.report", "reference/api/_autosummary/cyclops.report.report.ModelCardReport", "reference/api/_autosummary/cyclops.tasks.classification", "reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask", "reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask", "reference/api/cyclops.data", "reference/api/cyclops.monitor", "reference/api/cyclops.report", "reference/api/cyclops.tasks", "reference/api/evaluator", "reference/api/fairness_evaluator", "reference/api/metrics", "reference/api/metrics/accuracy", "reference/api/metrics/auroc", "reference/api/metrics/average_precision", "reference/api/metrics/f1score", "reference/api/metrics/f_beta", "reference/api/metrics/precision", "reference/api/metrics/precision_recall_curve", "reference/api/metrics/recall", "reference/api/metrics/roc", "reference/api/metrics/sensitivity", "reference/api/metrics/specificity", "reference/api/metrics/stat_scores", "tutorials", "tutorials_monitor", "tutorials_use_cases", "user_guide"], "filenames": ["api.rst", "contributing.rst", "developing.rst", "evaluation.rst", "index.rst", "installation.rst", "model_report.rst", "monitoring.rst", "reference/api/_autosummary/cyclops.data.aggregate.rst", "reference/api/_autosummary/cyclops.data.aggregate.Aggregator.rst", "reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.rst", "reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.rst", "reference/api/_autosummary/cyclops.data.features.medical_image.rst", "reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.rst", "reference/api/_autosummary/cyclops.data.slicer.rst", "reference/api/_autosummary/cyclops.data.slicer.SliceSpec.rst", "reference/api/_autosummary/cyclops.data.slicer.compound_filter.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_datetime.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_non_null.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_range.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_value.rst", "reference/api/_autosummary/cyclops.data.slicer.is_datetime.rst", "reference/api/_autosummary/cyclops.data.slicer.overall.rst", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.rst", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.rst", "reference/api/_autosummary/cyclops.report.report.rst", "reference/api/_autosummary/cyclops.report.report.ModelCardReport.rst", "reference/api/_autosummary/cyclops.tasks.classification.rst", "reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.rst", "reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.rst", "reference/api/cyclops.data.rst", "reference/api/cyclops.monitor.rst", "reference/api/cyclops.report.rst", "reference/api/cyclops.tasks.rst", "reference/api/evaluator.rst", "reference/api/fairness_evaluator.rst", "reference/api/metrics.rst", "reference/api/metrics/accuracy.rst", "reference/api/metrics/auroc.rst", "reference/api/metrics/average_precision.rst", "reference/api/metrics/f1score.rst", "reference/api/metrics/f_beta.rst", "reference/api/metrics/precision.rst", "reference/api/metrics/precision_recall_curve.rst", "reference/api/metrics/recall.rst", "reference/api/metrics/roc.rst", "reference/api/metrics/sensitivity.rst", "reference/api/metrics/specificity.rst", "reference/api/metrics/stat_scores.rst", "tutorials.rst", "tutorials_monitor.rst", "tutorials_use_cases.rst", "user_guide.rst"], "titles": ["API Reference", "Contributing to cyclops", "\ud83e\uddd1\ud83c\udfff\u200d\ud83d\udcbb Developing", "Evaluation", "What is CyclOps?", "Installation", "Model Report", "Monitoring", "cyclops.data.aggregate", "cyclops.data.aggregate.Aggregator", "cyclops.data.aggregate.tabular_as_aggregated", "cyclops.data.aggregate.timestamp_ffill_agg", "cyclops.data.features.medical_image", "cyclops.data.features.medical_image.MedicalImage", "cyclops.data.slicer", "cyclops.data.slicer.SliceSpec", "cyclops.data.slicer.compound_filter", "cyclops.data.slicer.filter_datetime", "cyclops.data.slicer.filter_non_null", "cyclops.data.slicer.filter_range", "cyclops.data.slicer.filter_string_contains", "cyclops.data.slicer.filter_value", "cyclops.data.slicer.is_datetime", "cyclops.data.slicer.overall", "cyclops.monitor.clinical_applicator", "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator", "cyclops.monitor.synthetic_applicator", "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator", "cyclops.monitor.synthetic_applicator.binary_noise_shift", "cyclops.monitor.synthetic_applicator.feature_association_shift", "cyclops.monitor.synthetic_applicator.feature_swap_shift", "cyclops.monitor.synthetic_applicator.gaussian_noise_shift", "cyclops.monitor.synthetic_applicator.knockout_shift", "cyclops.report.report", "cyclops.report.report.ModelCardReport", "cyclops.tasks.classification", "cyclops.tasks.classification.BinaryTabularClassificationTask", "cyclops.tasks.classification.MultilabelImageClassificationTask", "cyclops.data", "cyclops.monitor", "cyclops.report", "cyclops.tasks", "Evaluator", "Fairness Evaluator", "Evaluation Metrics", "Accuracy", "AUROC", "Average Precision", "F1-Score", "F-beta Score", "Precision", "PrecisionRecallCurve", "Recall", "ROC Curve", "Sensitivity", "Specificity", "StatScores", "Tutorials", "monitor API", "Example use cases", "User Guide"], "terms": {"cyclop": [0, 2, 5, 6, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "data": [0, 3, 4, 6, 7, 25, 27, 28, 29, 30, 31, 32, 34, 36, 37, 45, 48, 49, 51, 53, 57, 58], "task": [0, 4, 42, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 59], "evalu": [0, 4, 6, 7, 34, 36, 37, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 59, 60], "fair": [0, 3, 4, 34, 36, 37, 42, 60], "metric": [0, 3, 4, 6, 7, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "monitor": [0, 4, 6, 60], "report": [0, 4, 7, 56, 60], "thank": 1, "your": [1, 3, 6], "interest": 1, "To": [1, 7, 13], "submit": 1, "pr": 1, "pleas": 1, "fill": [1, 11], "out": [1, 34], "templat": [1, 34], "along": [1, 25], "If": [1, 2, 9, 11, 13, 15, 17, 18, 19, 20, 21, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "fix": 1, "an": [1, 4, 6, 7, 9, 13, 15, 34, 42, 43, 48, 50, 52, 54, 56, 59], "issu": [1, 43], "don": 1, "t": [1, 6, 13, 34, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "forget": 1, "link": [1, 6, 34], "onc": [1, 7], "python": 1, "virtual": 1, "environ": [1, 2], "i": [1, 2, 5, 6, 7, 9, 10, 11, 13, 15, 17, 18, 19, 20, 21, 22, 25, 27, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 59], "setup": 1, "you": [1, 2, 3, 6, 7, 13, 58], "can": [1, 2, 5, 6, 7, 11, 13, 15, 34, 36, 37, 43, 45, 48, 50, 52, 53, 54, 56, 58], "run": [1, 2], "us": [1, 4, 6, 7, 9, 13, 15, 16, 25, 27, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60], "all": [1, 6, 10, 11, 15, 16, 17, 18, 19, 20, 21, 23, 34, 36, 52, 54, 56], "file": [1, 7, 13, 34], "For": [1, 5, 34, 43, 56], "style": 1, "we": [1, 7, 34], "recommend": 1, "pep": 1, "8": [1, 15, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "guid": [1, 4, 6], "docstr": 1, "numpi": [1, 11, 28, 29, 30, 31, 32, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "format": [1, 9, 13, 15, 16, 17, 18, 19, 20, 21, 23, 34, 36, 37, 42, 43, 51, 56], "ruff": 1, "static": [1, 10], "analysi": [1, 34, 60], "check": [1, 6, 22, 51], "variou": [1, 6, 42], "rule": 1, "includ": [1, 9, 15, 19, 25, 27, 34, 43, 58, 59], "flake8": 1, "The": [1, 2, 3, 4, 6, 7, 9, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 25, 27, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59], "show": [1, 4], "error": [1, 42, 43], "which": [1, 9, 11, 17, 18, 19, 20, 21, 34, 42, 43, 51, 59], "need": [1, 2, 6, 42, 43], "befor": [1, 11, 36, 42, 43], "last": [1, 6, 10], "least": 1, "type": [1, 9, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "hint": 1, "our": 1, "mypi": 1, "set": [2, 5, 7, 9, 10, 15, 34, 42, 43, 45, 47, 48, 49, 50, 52, 54, 55, 56], "up": 2, "henc": [2, 5], "make": 2, "sure": 2, "instal": [2, 4, 60], "python3": [2, 5], "m": [2, 5], "sourc": [2, 4, 9, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58], "env": 2, "info": 2, "path": [2, 13, 25, 34, 36, 37], "bin": [2, 9, 43], "activ": 2, "In": [2, 6], "order": [2, 13, 42, 56], "depend": [2, 5, 6], "test": [2, 7, 27, 34, 36, 37, 42, 58], "codestyl": 2, "unit": 2, "integr": 2, "api": [2, 3, 4, 9, 42, 43, 59], "document": [2, 6], "built": 2, "sphinx": 2, "local": 2, "doc": 2, "cd": 2, "html": [2, 34], "sphinxopt": 2, "d": [2, 25], "nbsphinx_allow_error": 2, "true": [2, 9, 10, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 29, 34, 36, 37, 42, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56], "build": [2, 25], "pandoc": 2, "addit": [2, 5, 34, 36, 37], "group": [2, 6, 9, 34, 43], "welcom": 2, "see": [2, 6, 7, 9, 15, 34], "guidelin": [2, 4], "equip": 3, "rich": 3, "toolbox": 3, "assess": [3, 34], "model": [3, 4, 5, 7, 33, 34, 36, 37, 42, 43, 59, 60], "across": [3, 6, 29, 43, 58], "dimens": 3, "dive": [3, 6], "detail": [3, 15, 60], "perform": [3, 4, 6, 9, 11, 21, 34, 42, 45, 46, 58, 59, 60], "unveil": 3, "potenti": 3, "concern": 3, "gain": [3, 6], "granular": 3, "insight": [3, 6], "through": [3, 11], "slice": [3, 6, 15, 16, 34, 36, 37, 42, 43], "emploi": 3, "robust": [3, 4], "select": [3, 6, 7, 15, 25, 29], "common": 3, "": [3, 4, 6, 7, 15, 18, 20, 22, 34, 36, 37, 42, 43, 47], "effect": 3, "identifi": [3, 34], "area": [3, 46], "improv": [3, 4], "isol": 3, "behavior": 3, "specif": [3, 4, 5, 6, 15, 36, 37, 42, 44, 47], "subset": [3, 14], "reveal": 3, "nuanc": 3, "demograph": 3, "featur": [3, 6, 7, 15, 18, 21, 25, 29, 30, 34, 36, 59], "other": 3, "import": [3, 15, 25, 27, 34, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "characterist": 3, "uncov": 3, "analyz": 3, "bias": [3, 6], "within": [3, 29], "ensur": [3, 6], "respons": 3, "equit": 3, "outcom": [3, 6], "follow": [3, 6, 9, 15, 34, 42, 45, 48, 49, 50, 51, 52, 54, 55], "exampl": [3, 4, 5, 6, 9, 13, 15, 16, 17, 18, 19, 20, 21, 23, 25, 27, 34, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57], "below": [3, 5, 6, 7], "instruct": [3, 6], "open": 4, "toolkit": 4, "scientist": [4, 6], "engin": 4, "clinician": [4, 6], "work": [4, 15, 34, 45, 55], "intersect": [4, 15, 43], "artifici": 4, "intellig": 4, "health": 4, "design": 4, "help": [4, 6], "research": 4, "practition": 4, "adopt": 4, "vector": 4, "institut": 4, "ai": 4, "trust": 4, "safeti": 4, "principl": 4, "focus": 4, "system": 4, "develop": [4, 6], "clinic": [4, 24, 25, 58], "applic": [4, 24, 25, 27, 46], "where": [4, 6, 15, 16, 17, 18, 19, 20, 21, 34, 36, 37, 50, 51, 52, 54, 59], "ar": [4, 5, 6, 7, 13, 15, 19, 20, 25, 29, 34, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "critic": 4, "primari": [4, 34], "goal": [4, 59], "transpar": 4, "account": [4, 45, 46, 47, 48, 49, 50, 52, 54, 55], "heart": [4, 57], "failur": [4, 57], "predict": [4, 6, 36, 37, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57], "case": [4, 6, 28, 34, 57, 58], "how": [4, 6, 7, 9], "its": [4, 6, 15, 50, 52, 54], "over": [4, 6, 7, 34, 56], "time": [4, 6, 7, 9, 15, 25, 34, 58], "user": [4, 6, 7, 34], "tutori": [4, 59], "poetri": 4, "contribut": 4, "pre": 4, "commit": 4, "hook": 4, "code": [4, 6, 7], "refer": [4, 6, 34], "pycyclop": 5, "ha": [5, 7, 21, 34], "mani": 5, "option": [5, 7, 9, 10, 11, 13, 15, 17, 18, 19, 20, 21, 25, 27, 34, 36, 37, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "function": [5, 8, 9, 11, 13, 14, 15, 16, 26, 34, 42, 43], "monai": [5, 13], "librari": [5, 36, 37, 42, 43], "load": [5, 34, 36, 37, 42], "dicom": 5, "imag": [5, 12, 13, 31, 34, 37, 38, 42, 43, 57], "creat": [5, 14, 15, 28, 31, 32, 34, 36, 37, 43, 50, 52, 54], "dataset": [5, 14, 15, 16, 17, 18, 19, 20, 21, 23, 25, 27, 34, 36, 37, 38, 42, 43, 45, 48, 49, 50, 51, 52, 53, 54, 55, 58, 59, 60], "list": [5, 9, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 28, 29, 30, 31, 32, 34, 36, 37, 42, 43, 46, 47, 51, 53], "extra": [5, 6, 34], "note": [5, 9, 18, 34, 42], "xgboost": 5, "allow": [5, 6, 7, 42, 43, 58], "torch": [5, 36, 37, 42, 43], "pytorch": 5, "torchvis": 5, "torchxrayvis": 5, "transform": [5, 9, 36, 37, 46, 53], "alibi": 5, "explain": [5, 34], "detect": [5, 58], "shift": [5, 24, 25, 27, 29, 30], "technician": 6, "understand": 6, "better": [6, 7], "offer": 6, "clear": 6, "visual": [6, 7], "effortlessli": 6, "incorpor": 6, "gener": [6, 7, 15, 25, 34, 53, 58, 59], "figur": [6, 7, 34], "from": [6, 9, 13, 15, 25, 27, 32, 34, 36, 37, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 59], "provid": [6, 9, 15, 17, 20, 34, 42, 43, 51, 53, 56], "pictur": 6, "everyon": 6, "spec": 6, "view": [6, 7, 59], "relev": [6, 34, 58], "easi": 6, "interact": [6, 34], "explor": 6, "differ": [6, 7, 15, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55], "subgroup": [6, 60], "plot": [6, 7, 34], "adjust": 6, "displai": 6, "let": 6, "kei": [6, 15, 34, 43, 60], "section": [6, 34], "what": 6, "each": [6, 7, 9, 15, 16, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "one": [6, 9, 15, 36, 37, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55], "tell": 6, "u": [6, 57], "have": [6, 7, 17, 20, 21, 27, 59], "ad": [6, 7, 9, 27, 34, 36, 37], "mai": [6, 42, 43], "thi": [6, 7, 10, 15, 34, 36, 42, 43, 45, 46, 47, 48, 49, 50, 52, 54, 55, 56, 59], "comprehens": 6, "color": [6, 7], "quick": [6, 7], "identif": 6, "ani": [6, 11, 15, 17, 20, 21, 34, 36, 37, 42, 43], "signific": 6, "chang": [6, 34], "tailor": 6, "number": [6, 7, 9, 10, 11, 25, 29, 30, 34, 36, 37, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58], "suit": 6, "addition": 6, "access": [6, 13], "brief": 6, "descript": [6, 34], "e": [6, 9, 10, 15, 17, 18, 29, 30, 31, 34, 42, 43], "g": [6, 9, 10, 15, 17, 29, 30, 31, 34, 42, 43], "accuraci": [6, 44], "f1": [6, 44], "score": [6, 44, 45, 46, 47, 50, 51, 52, 53, 54, 55, 56], "hover": 6, "correspond": [6, 7, 36, 37, 51], "titl": 6, "conveni": 6, "add": [6, 15, 34, 36, 37], "multipl": [6, 7, 16, 34, 42, 43], "singl": [6, 7, 9, 11, 15, 34, 36, 37, 42, 51, 59], "timestamp": [6, 7, 9, 34], "x": [6, 7, 27, 28, 29, 30, 31, 32, 36, 57], "axi": [6, 7, 30], "shown": [6, 7], "distinct": 6, "ll": 6, "find": [6, 21, 36, 45, 46, 47, 48, 49, 50, 52, 54, 55], "abl": [6, 7], "distribut": 6, "By": 6, "part": 6, "about": [6, 7, 34], "also": [6, 45, 47, 48, 49, 50, 52, 54, 55, 59], "zoom": 6, "pan": 6, "further": 6, "investig": 6, "result": [6, 10, 16, 36, 37, 42, 43, 48], "comparison": 6, "chart": 6, "handi": 6, "tool": 6, "compar": 6, "them": [6, 45, 55, 58], "independ": 6, "sensit": [6, 34, 44, 58], "attribut": [6, 13, 15, 36, 37], "like": [6, 45, 50, 52, 54], "ag": [6, 7, 25], "race": 6, "gender": 6, "ideal": 6, "should": [6, 9, 15, 16, 30, 34, 36, 37, 42, 43, 53], "same": [6, 29, 42, 43], "isn": 6, "toward": 6, "against": 6, "particular": 6, "here": [6, 15], "metadata": [6, 9, 13], "owner": [6, 34], "extern": 6, "repositori": [6, 13, 59], "paper": 6, "entail": 6, "inform": [6, 34], "ethic": 6, "risk": [6, 34, 59], "etc": [6, 29, 30], "after": 7, "initi": [7, 36, 37, 45], "accomplish": 7, "new": [7, 9, 34], "trend": [7, 34], "latest": [7, 9], "everytim": 7, "entri": [7, 34, 56], "json": [7, 34], "do": [7, 15], "so": 7, "repeatedli": 7, "dai": [7, 15, 17, 59], "week": 7, "month": [7, 15, 17, 25], "At": 7, "top": [7, 45, 47, 48, 49, 50, 52, 54, 55, 56], "level": [7, 43], "glanc": 7, "overal": [7, 15, 34, 43], "left": [7, 34], "indic": [7, 15, 28, 31, 42, 43, 45, 50, 52, 54], "valu": [7, 9, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 25, 34, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "base": [7, 9, 13, 15, 25, 27, 34, 36, 37, 42, 43, 50, 52, 54], "minimum": [7, 15, 19, 34], "threshold": [7, 34, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "wa": [7, 9], "drop": 7, "accept": [7, 9], "rang": [7, 15, 19, 46, 47, 51, 53], "red": 7, "when": [7, 13, 36, 37, 42, 43, 45, 47, 48, 49, 50, 52, 54, 55, 56], "everyth": 7, "good": 7, "appear": 7, "green": 7, "get": [7, 36, 37], "prespect": 7, "interv": 7, "sex": [7, 25], "again": 7, "past": 7, "slider": 7, "right": 7, "class": [8, 9, 12, 13, 14, 15, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 36, 37, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "aggfunc": 9, "timestamp_col": 9, "time_bi": 9, "agg_bi": 9, "timestep_s": 9, "none": [9, 10, 11, 13, 15, 17, 25, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "window_dur": 9, "window_start_tim": 9, "window_stop_tim": 9, "imput": 9, "agg_meta_for": 9, "transformermixin": 9, "equal": [9, 15, 19, 43], "space": [9, 53], "seri": [9, 11, 36], "comput": [9, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "expens": [9, 42, 43], "done": 9, "sparingli": 9, "map": [9, 15, 16, 36, 37], "column": [9, 10, 15, 16, 17, 18, 19, 20, 21, 25, 36, 37, 42, 43], "either": [9, 19, 34, 56], "string": [9, 13, 15, 17, 20, 34, 42, 43], "col_nam": 9, "mean": [9, 34, 45, 46, 47, 48, 49, 50, 52, 54, 55], "return": [9, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "dict": [9, 13, 15, 34, 36, 37, 42, 43], "name": [9, 10, 15, 16, 17, 18, 19, 20, 21, 25, 34, 36, 37, 42, 43, 51, 53], "str": [9, 10, 13, 15, 17, 18, 19, 20, 21, 22, 25, 27, 34, 36, 37, 42, 43], "determin": [9, 45, 46, 47, 51, 53, 56], "bucket": [9, 11], "hour": [9, 15, 17], "timestep": [9, 10, 11], "float": [9, 11, 18, 19, 28, 29, 30, 31, 32, 34, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "window": [9, 11, 58], "stop": 9, "start": 9, "pd": [9, 10, 36], "datafram": [9, 10, 36], "window_tim": 9, "aggregatedimput": 9, "num_timestep": [9, 10, 11], "int": [9, 10, 11, 15, 17, 25, 29, 30, 31, 32, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "dictionari": [9, 13, 15, 16, 34, 36, 37, 42, 43], "median": 9, "std": 9, "var": 9, "min": 9, "max": [9, 10], "count": [9, 45, 46, 47, 48, 49, 50, 52, 54, 55], "sum": [9, 55, 56], "init": 9, "method": [9, 13, 15, 25, 27, 34, 36, 37, 42, 43, 46], "__init__": [9, 36, 37], "fit": [9, 36], "paramet": [9, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 60], "panda": [9, 10, 11], "input": [9, 13, 28, 31, 36, 37, 45, 47, 50, 51, 52, 53, 54, 56], "fit_transform": 9, "set_output": 9, "output": [9, 34, 45, 51, 53], "contain": [9, 10, 13, 15, 16, 17, 18, 19, 20, 21, 23, 34, 42, 43, 45, 51, 53, 55, 59], "introduc": 9, "default": [9, 11, 13, 15, 17, 18, 19, 20, 21, 25, 34, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "polar": 9, "configur": [9, 36, 37, 42], "unchang": 9, "version": [9, 13, 34], "1": [9, 11, 15, 25, 29, 30, 31, 32, 34, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 59], "4": [9, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "self": [9, 13], "estim": [9, 36, 45, 46, 48, 49, 51, 53, 55], "instanc": [9, 45, 46, 47, 48, 49, 50, 52, 54, 55], "y": [9, 27, 29, 30, 32, 36], "include_timestep_start": 9, "placehold": [9, 29, 32], "sklearn": [9, 27], "compat": 9, "bool": [9, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 29, 30, 34, 36, 37, 42, 43, 56], "whether": [9, 13, 15, 25, 34, 42, 43, 56, 59], "tab": 10, "index": 10, "var_nam": 10, "value_nam": 10, "strategi": [10, 34], "sort": 10, "pose": 10, "tabular": [10, 36, 37, 57], "non": [10, 51, 53], "timeseri": 10, "origin": [10, 25], "fake": 10, "first": [10, 42, 43, 45, 55], "requir": [10, 34, 36, 37, 43, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56], "process": [10, 25], "tempor": 10, "val": [11, 42], "fill_nan": 11, "forward": 11, "given": [11, 17, 19, 20, 21, 22, 34, 36, 37, 45, 48, 49, 50, 52, 54, 55, 56], "neg": [11, 42, 45, 46, 47, 48, 49, 50, 52, 54, 55, 56], "treat": [11, 43], "occur": 11, "regular": 11, "between": [11, 15, 43, 48, 53, 56], "0": [11, 15, 27, 28, 29, 30, 31, 32, 34, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "accordingli": 11, "nan": [11, 18, 42, 43], "A": [11, 15, 16, 17, 18, 19, 20, 21, 23, 34, 42, 43, 45, 51, 53, 55, 56], "integ": [11, 15, 34, 42, 43], "total": [11, 45, 46, 47, 48, 49, 50, 52, 54, 55, 56], "consid": [11, 18, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "remain": 11, "form": [11, 48], "2": [11, 15, 29, 30, 34, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56], "dimension": [11, 58], "arrai": [11, 13, 29, 30, 32, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "ndarrai": [11, 13, 22, 28, 29, 30, 31, 32, 36, 37, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "medic": [12, 13, 38, 59], "mode": 13, "decod": 13, "id": [13, 25], "reader": 13, "itkread": 13, "suffix": 13, "jpg": 13, "read": 13, "union": [13, 15, 18, 21, 22, 34, 36, 37, 42, 43, 46, 51], "imageread": 13, "byte": 13, "fals": [13, 15, 17, 18, 19, 20, 21, 22, 30, 31, 36, 37, 42, 43, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56], "image_path": 13, "image_byt": 13, "__call__": 13, "call": [13, 34], "cast_storag": 13, "storag": 13, "cast": 13, "arrow": 13, "convert": [13, 45, 47, 49, 51, 52, 53, 54, 55, 56], "pyarrow": [13, 16, 17, 18, 19, 20, 21, 23], "rtype": 13, "structarrai": 13, "pa": 13, "must": [13, 17, 34, 42, 43, 47], "binari": [13, 28, 36, 37, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 59], "struct": 13, "doesn": 13, "matter": 13, "arg": 13, "stringarrai": 13, "listarrai": 13, "decode_exampl": 13, "token_per_repo_id": 13, "serial": 13, "privat": 13, "hub": 13, "pass": [13, 15, 25, 34, 36, 37, 42], "repo_id": 13, "token": 13, "deseri": 13, "np": [13, 19, 22, 36, 37, 43], "embed_storag": 13, "emb": 13, "encode_exampl": 13, "encod": 13, "flatten": 13, "state": [13, 51], "itself": 13, "otherwis": [13, 22, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56], "tupl": [13, 15, 25, 36, 37, 51, 53], "classlabel": 13, "translat": 13, "translationvariablelanguag": 13, "sequenc": [13, 42], "array2d": 13, "array3d": 13, "array4d": 13, "array5d": 13, "audio": 13, "hug": [14, 36, 37], "face": [14, 36, 37], "spec_list": 15, "factori": 15, "valid": [15, 17, 34, 36, 37, 42], "include_overal": 15, "column_nam": [15, 17, 18, 19, 20, 21], "object": [15, 25, 27, 34, 36, 37, 43], "more": [15, 36, 37, 42, 59], "exact": [15, 21], "row": 15, "support": [15, 45, 46, 47, 48, 49, 50, 52, 54, 55, 56, 58], "2021": 15, "01": [15, 46], "00": 15, "min_valu": [15, 19], "specifi": [15, 25, 28, 31, 34, 36, 37, 42], "min_inclus": [15, 19], "numer": [15, 19], "datetim": [15, 17, 19, 22, 34], "inf": [15, 19], "max_valu": [15, 19], "boolean": [15, 16, 17, 18, 19, 20, 21, 23], "greater": 15, "than": [15, 19, 36, 37, 49, 59], "maximum": [15, 19, 46], "max_inclus": [15, 19], "less": [15, 19, 49], "year": [15, 17, 57], "match": [15, 17, 20, 21, 42], "12": 15, "31": 15, "23": 15, "negat": [15, 17, 18, 19, 20, 21], "flag": 15, "doe": [15, 17, 19, 20, 21, 34, 43, 45, 46, 47, 48, 49, 50, 52, 54, 55], "keep_nul": [15, 17, 19, 20, 21], "keep": [15, 42, 43], "null": [15, 17, 18, 19, 20, 21], "conjunct": 15, "own": 15, "argument": [15, 36, 37, 42, 43], "r": 15, "itertool": 15, "combin": [15, 16], "_before_": 15, "regist": 15, "_registri": 15, "callabl": [15, 16, 34, 43], "slice_spec": [15, 36, 37, 42], "feature_1": 15, "feature_2": 15, "feature_3": 15, "value_1": 15, "value_2": 15, "2020": [15, 17], "5": [15, 27, 28, 31, 32, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "60": 15, "2022": 15, "6": [15, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "7": [15, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 59], "2000": 15, "2010": 15, "slice_nam": [15, 34], "slice_func": 15, "print": 15, "someth": 15, "filter": [15, 17, 18, 19, 20, 21, 42, 43], "non_nul": 15, "wai": 15, "compound": 15, "add_slice_spec": 15, "get_slic": 15, "registri": 15, "slice_funct": 16, "bitwis": 16, "AND": 16, "tabl": [16, 17, 18, 19, 20, 21, 23], "lazybatch": [16, 17, 18, 19, 20, 21, 23], "appli": [16, 25, 36, 37, 45, 46, 50, 51, 52, 53, 54, 55, 56], "signatur": 16, "kwarg": [16, 27, 36, 37], "batch": [17, 18, 19, 20, 21, 23, 25, 36, 37, 42, 43], "compon": 17, "rais": [17, 19, 20, 34, 36, 37, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "typeerror": [17, 19, 20, 34, 43], "point": [18, 56], "valueerror": [19, 34, 36, 37, 42, 43, 45, 47, 49, 50, 51, 52, 53, 54, 55, 56], "substr": 20, "datetime64": 22, "modul": [24, 33, 34, 42, 43], "shift_typ": [25, 27], "target": [25, 29, 30, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59], "shift_id": 25, "induc": [25, 27], "synthet": [25, 27, 34, 59], "take": [25, 43, 45, 46, 47, 48, 49, 50, 52, 54, 55], "split": [25, 34, 36, 37, 42], "categor": 25, "loader": 25, "load_nihcxr": 25, "mnt": 25, "nihcxr": 25, "hospital_typ": 25, "hospital_type_1": 25, "hospital_type_2": 25, "hospital_type_3": 25, "hospital_type_4": 25, "hospital_type_5": 25, "ds_sourc": 25, "ds_target": 25, "apply_shift": [25, 27], "custom": [25, 34], "batch_siz": [25, 36, 37, 42, 43], "1000": [25, 36, 42, 43], "num_proc": 25, "huggingfac": [25, 36, 37, 43], "size": [25, 36, 37, 42, 43], "slicespec": [25, 37, 42], "hospit": [25, 57], "syntheticshiftappl": 26, "load_diabet": 27, "return_x_i": 27, "from_dict": 27, "train_test_split": 27, "test_siz": 27, "seed": 27, "42": 27, "gn_shift": 27, "x_shift": 27, "ko_shift": 27, "cp_shift": 27, "mfa_shift": 27, "bn_shift": 27, "tolerance_shift": 27, "ds_shift": 27, "nois": [27, 28, 31], "prob": 28, "delta": [28, 31, 32], "matrix": [28, 29, 30, 31, 32], "covari": [28, 29, 30, 31, 32], "p": [28, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "proport": 28, "control": [28, 43], "fraction": [28, 31, 32], "affect": [28, 31, 34], "dtype": [28, 29, 30, 31, 32], "float64": [28, 29, 30, 31, 32], "n_shuffl": [29, 30], "25": [29, 30, 46, 51, 53, 55, 56], "keep_rows_const": 29, "repermute_each_column": 29, "multiwai": 29, "associ": 29, "swap": [29, 30], "individu": 29, "label": [29, 30, 32, 36, 37, 42, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 59], "cl": [29, 30], "3": [29, 30, 34, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "floatnumpi": 29, "shuffl": [29, 30], "permut": 29, "shift_class": [30, 32], "rank": 30, "changepoint": 30, "x_ref": 30, "y_ref": 30, "noise_amt": 31, "normal": 31, "clip": 31, "gaussian": 31, "standard": [31, 34], "deviat": [31, 34], "divid": 31, "255": 31, "imbal": [32, 45, 46, 47, 48, 49, 50, 52, 54, 55], "remov": [32, 36, 37, 43], "sampl": [32, 50, 52, 54], "output_dir": 34, "card": 34, "serv": 34, "interfac": 34, "popul": 34, "modelcard": 34, "directori": [34, 36, 37], "save": [34, 36, 37], "current": 34, "export": 34, "output_filenam": 34, "template_path": 34, "save_json": 34, "last_n_ev": 34, "mean_std_min_ev": 34, "synthetic_timestamp": 34, "date": 34, "jinja2": 34, "most": 34, "recent": 34, "calcul": [34, 45, 46, 47, 48, 49, 50, 52, 54, 55], "overview": [34, 60], "back": 34, "classmethod": 34, "from_json_fil": 34, "cyclops_report": 34, "log_cit": 34, "citat": 34, "section_nam": 34, "model_detail": 34, "content": 34, "repres": 34, "keyerror": 34, "bibtex": 34, "plain": 34, "text": 34, "log_dataset": 34, "license_id": 34, "sensitive_featur": 34, "sensitive_feature_justif": 34, "log": [34, 42], "train": [34, 36, 37, 42], "resourc": 34, "context": 34, "homepag": 34, "spdx": 34, "licens": 34, "apach": 34, "http": 34, "org": 34, "unknown": 34, "unlicens": 34, "proprietari": 34, "justif": 34, "field": 34, "assertionerror": [34, 51, 53], "log_descriptor": 34, "descriptor": 34, "pydant": 34, "basemodel": 34, "subclass": 34, "As": 34, "long": 34, "conflict": 34, "defin": 34, "model_card": 34, "tradeoff": 34, "trade": 34, "off": 34, "interpret": 34, "consider": [34, 60], "log_fairness_assess": 34, "affected_group": 34, "benefit": 34, "harm": 34, "mitigation_strategi": 34, "mitig": 34, "relat": 34, "log_from_dict": 34, "log_imag": 34, "img_path": 34, "caption": 34, "exist": [34, 36, 37], "log_licens": 34, "full": 34, "avail": [34, 42, 59], "whole": 34, "found": 34, "blank": 34, "instead": 34, "log_model_paramet": 34, "param": 34, "log_own": 34, "contact": 34, "role": 34, "log_performance_metr": 34, "quantit": [34, 60], "slash": 34, "metric_nam": [34, 43], "log_plotly_figur": 34, "fig": 34, "plotli": 34, "log_quantitative_analysi": 34, "analysis_typ": 34, "metric_slic": 34, "decision_threshold": 34, "pass_fail_threshold": 34, "pass_fail_threshold_fn": 34, "liter": [34, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "decis": [34, 46, 51, 53], "fail": 34, "log_refer": 34, "log_regul": 34, "regul": 34, "regulatori": 34, "compli": 34, "log_risk": 34, "log_use_cas": 34, "kind": 34, "scope": 34, "usecas": 34, "log_us": 34, "log_vers": 34, "version_str": 34, "semant": 34, "v1": 34, "dt_date": 34, "dt_datetim": 34, "unix": 34, "yyyi": 34, "mm": 34, "dd": 34, "hh": 34, "ss": 34, "ffffff": 34, "z": 34, "summar": 34, "made": 34, "task_featur": [36, 37], "task_target": [36, 37], "basetask": [36, 37], "add_model": [36, 37], "wrappedmodel": [36, 37], "properti": [36, 37], "data_typ": [36, 37], "model_nam": [36, 37], "prediction_column_prefix": [36, 37], "splits_map": [36, 37], "remove_column": [36, 37, 42, 43], "fairness_config": [36, 37, 42], "override_fairness_metr": [36, 37, 42], "array_lib": [36, 37, 42, 43], "datasetdict": [36, 37, 42], "metricdict": [36, 37, 42, 43], "columntransform": 36, "slicingconfig": 36, "config": 36, "default_max_batch_s": 36, "unnecessari": [36, 37], "fairnessconfig": [36, 37, 42], "overridden": [36, 37, 42], "cupi": [36, 37, 42, 43], "get_model": [36, 37], "list_model": [36, 37], "list_models_param": [36, 37], "load_model": [36, 37], "filepath": [36, 37], "pretrain": [36, 37], "models_count": [36, 37], "proba": 36, "mortal": [36, 57], "probabl": [36, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "notfittederror": 36, "save_model": [36, 37], "destin": [36, 37], "parent": [36, 37], "keyword": [36, 37, 42, 43], "task_typ": [36, 37], "best_model_param": 36, "x_train": 36, "y_train": 36, "pipelin": 36, "best": 36, "64": 37, "compos": 37, "pathologi": 37, "represent": 37, "packag": [38, 39, 40, 41], "consist": [42, 43], "It": [42, 48, 50, 52, 54, 55, 58], "target_column": [42, 43], "prediction_column": [42, 43], "ignore_column": 42, "raise_on_empty_slic": [42, 43], "load_dataset_kwarg": 42, "load_dataset": 42, "multi": 42, "ignor": 42, "while": 42, "memori": [42, 43], "entir": 42, "chosen": 42, "eval": 42, "dev": 42, "empti": [42, 43], "warn": [42, 45, 47, 48, 49, 50, 52, 54, 55], "onli": [42, 43, 45, 46, 48, 49, 50, 52, 54, 55, 56], "evaluate_fair": 43, "pariti": 43, "group_valu": 43, "group_bin": 43, "group_base_valu": 43, "compute_optimal_threshold": 43, "metric_kwarg": 43, "multilabel": [43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "uniqu": [43, 46, 47, 51, 53, 59], "limit": 43, "continu": [43, 56], "veri": 43, "slow": 43, "larg": 43, "denomin": 43, "linspac": 43, "monoton": [43, 47, 53], "optim": 43, "oper": 43, "necessari": 43, "usag": 43, "rel": 43, "small": 43, "32": 43, "avoid": 43, "encount": 43, "nest": 43, "second": 43, "third": 43, "omit": 43, "runtimeerror": 43, "auroc": 44, "averag": [44, 45, 46, 48, 49, 50, 52, 54, 55], "precis": [44, 45, 48, 49, 51, 52, 53, 54, 55], "f": [44, 48], "beta": 44, "precisionrecallcurv": 44, "recal": [44, 45, 47, 48, 51, 53, 54, 55], "roc": [44, 46], "curv": [44, 46, 51], "statscor": 44, "classif": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57], "multiclass": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "One": [45, 46, 49, 50, 52, 53], "pos_label": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "posit": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "num_class": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "decid": [45, 48, 49, 50, 52, 54, 55], "top_k": [45, 47, 48, 49, 50, 52, 54, 55, 56], "logit": [45, 48, 49, 50, 51, 52, 54, 55, 56], "k": [45, 48, 49, 50, 52, 54, 55, 56], "num_label": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "micro": [45, 46, 47, 48, 49, 50, 52, 54, 55], "macro": [45, 46, 47, 48, 49, 50, 52, 54, 55], "weight": [45, 46, 47, 48, 49, 50, 52, 54, 55], "global": [45, 46, 47, 48, 49, 50, 52, 54, 55], "unweight": [45, 46, 47, 48, 49, 50, 52, 54, 55], "alter": [45, 48, 49, 50, 52, 54, 55], "zero_divis": [45, 47, 48, 49, 50, 52, 54, 55], "zero": [45, 47, 48, 49, 50, 52, 54, 55], "divis": [45, 47, 48, 49, 50, 52, 54, 55], "act": [45, 47, 48, 49, 50, 52, 54, 55, 56], "pred": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "75": [45, 46, 47, 51, 53, 54, 55], "reset_st": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "05": [45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56], "95": [45, 46, 48, 50, 51, 53], "zip": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "update_st": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "66666667": [45, 48, 51, 52, 53, 54, 55], "sigmoid": [45, 46, 49, 51, 53, 55, 56], "875": 45, "problem": [45, 51, 54, 56, 59], "highest": [45, 50, 52, 54, 55], "binar": [45, 46, 48, 53, 56], "classifi": [45, 48, 49, 50, 52, 54, 55], "correct": [45, 46, 48, 49, 50, 51, 52, 53, 54, 55], "per": 45, "npt": [45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56], "arraylik": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "ground": [45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56], "truth": [45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56], "acc_scor": 45, "9": [45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56], "5555555555555555": 45, "shape": [45, 46, 48, 49, 50, 51, 52, 54, 55, 56], "num_sampl": 45, "flot": 45, "6666666666666666": [45, 48, 50, 51], "max_fpr": 46, "rate": [46, 53], "partial": 46, "auc": 46, "automat": [46, 47, 51, 53], "under": 46, "35": [46, 47, 51, 53, 54, 55], "6111111111111112": 46, "89": [46, 53], "06": [46, 53], "94": 46, "22222222": 46, "625": [46, 49, 55], "aucroc": 46, "auroc_scor": 46, "thei": [46, 51, 53], "via": 46, "softmax": [46, 51, 53], "33333333": [46, 51, 52, 53, 54], "5833333333333333": 47, "1d": [47, 53], "increas": [47, 53], "85714286": 48, "harmon": 49, "8333333333333334": [49, 50, 52, 54], "9090909090909091": 49, "83333333": 49, "55555556": [49, 55], "90909091": 49, "85": [49, 50, 52, 54], "7142857142857143": 49, "appropri": [50, 52, 54], "ratio": [50, 52, 54, 55], "correctli": 50, "observ": [50, 59], "precision_scor": 50, "11": [50, 59], "22": [50, 56], "84": 50, "73": 50, "33": 50, "92": 50, "expect": [50, 52, 54], "n": [50, 52, 54], "l": [50, 52, 54], "presenc": [50, 52, 54], "absenc": [50, 52, 54], "suniqu": 51, "prcurv": 51, "45": [51, 54, 55], "42857143": 51, "375": 51, "multilaabel": 51, "prec": 51, "tolist": 51, "3333333333333333": [51, 52, 54], "rec": 51, "binary_precision_recall_curv": 51, "autofunct": [51, 52], "element": 51, "len": 51, "tp": [52, 54], "fn": [52, 54], "intuit": [52, 54], "abil": [52, 54], "recall_scor": 52, "binary_recal": 52, "precision_recal": 52, "rest": [52, 54], "evenli": 53, "fpr": 53, "tpr": 53, "els": 53, "02": 53, "03": 53, "sensitv": 54, "55": 54, "65": 54, "15": [55, 56], "57142857": 55, "stat": 56, "prior": 56, "classwis": 56, "labelwis": 56, "16": 56, "26": 56, "58": 56, "61": 56, "17": 56, "71": 56, "09": 56, "20": 56, "82": 56, "13": 56, "10": 56, "special": 56, "statist": [56, 58], "n_class": 56, "n_label": 56, "assum": 56, "nadarrai": 56, "kaggl": 57, "mimiciv": 57, "synthea": 57, "prolong": 57, "length": 57, "stai": 57, "diabet": 57, "130": 57, "1999": 57, "2008": 57, "readmiss": 57, "nih": 57, "chest": 57, "rai": 57, "drift": 58, "experi": 58, "reduct": 58, "techniqu": 58, "roll": 58, "diseas": 59, "variabl": 59, "ehr": 59, "collect": 59, "site": 59, "icu": 59, "patient": 59, "procedur": 59, "uci": 59, "machin": 59, "learn": 59, "47": 59, "showcas": 59, "implement": 59, "112": 59, "120": 59, "frontal": 59, "30": 59, "805": 59, "14": 59, "demonstr": 59, "pip": 60, "capabl": 60}, "objects": {"cyclops": [[38, 0, 0, "-", "data"], [39, 0, 0, "-", "monitor"], [40, 0, 0, "-", "report"], [41, 0, 0, "-", "tasks"]], "cyclops.data": [[8, 0, 0, "-", "aggregate"], [38, 0, 0, "-", "features"], [14, 0, 0, "-", "slicer"]], "cyclops.data.aggregate": [[9, 1, 1, "", "Aggregator"], [10, 4, 1, "", "tabular_as_aggregated"], [11, 4, 1, "", "timestamp_ffill_agg"]], "cyclops.data.aggregate.Aggregator": [[9, 2, 1, "", "__init__"], [9, 3, 1, "", "agg_by"], [9, 3, 1, "", "agg_meta_for"], [9, 3, 1, "", "aggfuncs"], [9, 2, 1, "", "fit"], [9, 2, 1, "", "fit_transform"], [9, 3, 1, "", "imputer"], [9, 3, 1, "", "num_timesteps"], [9, 2, 1, "", "set_output"], [9, 3, 1, "", "time_by"], [9, 3, 1, "", "timestamp_col"], [9, 3, 1, "", "timestep_size"], [9, 2, 1, "", "transform"], [9, 3, 1, "", "window_duration"], [9, 3, 1, "", "window_start_time"], [9, 3, 1, "", "window_stop_time"], [9, 3, 1, "", "window_times"]], "cyclops.data.features": [[12, 0, 0, "-", "medical_image"]], "cyclops.data.features.medical_image": [[13, 1, 1, "", "MedicalImage"]], "cyclops.data.features.medical_image.MedicalImage": [[13, 2, 1, "", "__call__"], [13, 2, 1, "", "cast_storage"], [13, 2, 1, "", "decode_example"], [13, 2, 1, "", "embed_storage"], [13, 2, 1, "", "encode_example"], [13, 2, 1, "", "flatten"]], "cyclops.data.slicer": [[15, 1, 1, "", "SliceSpec"], [16, 4, 1, "", "compound_filter"], [17, 4, 1, "", "filter_datetime"], [18, 4, 1, "", "filter_non_null"], [19, 4, 1, "", "filter_range"], [20, 4, 1, "", "filter_string_contains"], [21, 4, 1, "", "filter_value"], [22, 4, 1, "", "is_datetime"], [23, 4, 1, "", "overall"]], "cyclops.data.slicer.SliceSpec": [[15, 3, 1, "", "_registry"], [15, 2, 1, "", "add_slice_spec"], [15, 3, 1, "", "column_names"], [15, 2, 1, "", "get_slices"], [15, 3, 1, "", "include_overall"], [15, 2, 1, "", "slices"], [15, 3, 1, "", "spec_list"], [15, 3, 1, "", "validate"]], "cyclops.evaluate.evaluator": [[42, 4, 1, "", "evaluate"]], "cyclops.evaluate.fairness.evaluator": [[43, 4, 1, "", "evaluate_fairness"]], "cyclops.evaluate.metrics": [[46, 1, 1, "", "AUROC"], [45, 1, 1, "", "Accuracy"], [46, 1, 1, "", "BinaryAUROC"], [45, 1, 1, "", "BinaryAccuracy"], [47, 1, 1, "", "BinaryAveragePrecision"], [48, 1, 1, "", "BinaryF1Score"], [49, 1, 1, "", "BinaryFbetaScore"], [50, 1, 1, "", "BinaryPrecision"], [51, 1, 1, "", "BinaryPrecisionRecallCurve"], [53, 1, 1, "", "BinaryROCCurve"], [52, 1, 1, "", "BinaryRecall"], [54, 1, 1, "", "BinarySensitivity"], [55, 1, 1, "", "BinarySpecificity"], [56, 1, 1, "", "BinaryStatScores"], [48, 1, 1, "", "F1Score"], [49, 1, 1, "", "FbetaScore"], [46, 1, 1, "", "MulticlassAUROC"], [45, 1, 1, "", "MulticlassAccuracy"], [48, 1, 1, "", "MulticlassF1Score"], [49, 1, 1, "", "MulticlassFbetaScore"], [50, 1, 1, "", "MulticlassPrecision"], [51, 1, 1, "", "MulticlassPrecisionRecallCurve"], [53, 1, 1, "", "MulticlassROCCurve"], [52, 1, 1, "", "MulticlassRecall"], [54, 1, 1, "", "MulticlassSensitivity"], [55, 1, 1, "", "MulticlassSpecificity"], [56, 1, 1, "", "MulticlassStatScores"], [46, 1, 1, "", "MultilabelAUROC"], [45, 1, 1, "", "MultilabelAccuracy"], [48, 1, 1, "", "MultilabelF1Score"], [49, 1, 1, "", "MultilabelFbetaScore"], [50, 1, 1, "", "MultilabelPrecision"], [51, 1, 1, "", "MultilabelPrecisionRecallCurve"], [53, 1, 1, "", "MultilabelROCCurve"], [52, 1, 1, "", "MultilabelRecall"], [54, 1, 1, "", "MultilabelSensitivity"], [55, 1, 1, "", "MultilabelSpecificity"], [56, 1, 1, "", "MultilabelStatScores"], [50, 1, 1, "", "Precision"], [51, 1, 1, "", "PrecisionRecallCurve"], [53, 1, 1, "", "ROCCurve"], [52, 1, 1, "", "Recall"], [54, 1, 1, "", "Sensitivity"], [55, 1, 1, "", "Specificity"], [56, 1, 1, "", "StatScores"]], "cyclops.evaluate.metrics.functional.accuracy": [[45, 4, 1, "", "binary_accuracy"], [45, 4, 1, "", "multiclass_accuracy"], [45, 4, 1, "", "multilabel_accuracy"]], "cyclops.evaluate.metrics.functional.auroc": [[46, 4, 1, "", "auroc"], [46, 4, 1, "", "binary_auroc"], [46, 4, 1, "", "multiclass_auroc"], [46, 4, 1, "", "multilabel_auroc"]], "cyclops.evaluate.metrics.functional.average_precision": [[47, 4, 1, "", "average_precision"], [47, 4, 1, "", "binary_average_precision"]], "cyclops.evaluate.metrics.functional.f_beta": [[48, 4, 1, "", "binary_f1_score"], [49, 4, 1, "", "binary_fbeta_score"], [48, 4, 1, "", "f1_score"], [49, 4, 1, "", "fbeta_score"], [48, 4, 1, "", "multiclass_f1_score"], [49, 4, 1, "", "multiclass_fbeta_score"], [48, 4, 1, "", "multilabel_f1_score"], [49, 4, 1, "", "multilabel_fbeta_score"]], "cyclops.evaluate.metrics.functional.precision_recall": [[50, 4, 1, "", "binary_precision"], [50, 4, 1, "", "multiclass_precision"], [52, 4, 1, "", "multiclass_recall"], [50, 4, 1, "", "multilabel_precision"], [52, 4, 1, "", "multilabel_recall"], [50, 4, 1, "", "precision"], [52, 4, 1, "", "recall"]], "cyclops.evaluate.metrics.functional.precision_recall_curve": [[51, 4, 1, "", "multiclass_precision_recall_curve"], [51, 4, 1, "", "multilabel_precision_recall_curve"], [51, 4, 1, "", "precision_recall_curve"]], "cyclops.evaluate.metrics.functional.roc": [[53, 4, 1, "", "binary_roc_curve"], [53, 4, 1, "", "multiclass_roc_curve"], [53, 4, 1, "", "multilabel_roc_curve"], [53, 4, 1, "", "roc_curve"]], "cyclops.evaluate.metrics.functional.sensitivity": [[54, 4, 1, "", "binary_sensitivity"], [54, 4, 1, "", "multiclass_sensitivity"], [54, 4, 1, "", "multilabel_sensitivity"], [54, 4, 1, "", "sensitivity"]], "cyclops.evaluate.metrics.functional.specificity": [[55, 4, 1, "", "binary_specificity"], [55, 4, 1, "", "multiclass_specificity"], [55, 4, 1, "", "multilabel_specificity"], [55, 4, 1, "", "specificity"]], "cyclops.evaluate.metrics.functional.stat_scores": [[56, 4, 1, "", "binary_stat_scores"], [56, 4, 1, "", "multiclass_stat_scores"], [56, 4, 1, "", "multilabel_stat_scores"], [56, 4, 1, "", "stat_scores"]], "cyclops.monitor": [[24, 0, 0, "-", "clinical_applicator"], [26, 0, 0, "-", "synthetic_applicator"]], "cyclops.monitor.clinical_applicator": [[25, 1, 1, "", "ClinicalShiftApplicator"]], "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator": [[25, 2, 1, "", "age"], [25, 2, 1, "", "apply_shift"], [25, 2, 1, "", "custom"], [25, 2, 1, "", "hospital_type"], [25, 2, 1, "", "month"], [25, 2, 1, "", "sex"], [25, 2, 1, "", "time"]], "cyclops.monitor.synthetic_applicator": [[27, 1, 1, "", "SyntheticShiftApplicator"], [28, 4, 1, "", "binary_noise_shift"], [29, 4, 1, "", "feature_association_shift"], [30, 4, 1, "", "feature_swap_shift"], [31, 4, 1, "", "gaussian_noise_shift"], [32, 4, 1, "", "knockout_shift"]], "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator": [[27, 2, 1, "", "apply_shift"]], "cyclops.report": [[33, 0, 0, "-", "report"]], "cyclops.report.report": [[34, 1, 1, "", "ModelCardReport"]], "cyclops.report.report.ModelCardReport": [[34, 2, 1, "", "export"], [34, 2, 1, "", "from_json_file"], [34, 2, 1, "", "log_citation"], [34, 2, 1, "", "log_dataset"], [34, 2, 1, "", "log_descriptor"], [34, 2, 1, "", "log_fairness_assessment"], [34, 2, 1, "", "log_from_dict"], [34, 2, 1, "", "log_image"], [34, 2, 1, "", "log_license"], [34, 2, 1, "", "log_model_parameters"], [34, 2, 1, "", "log_owner"], [34, 2, 1, "", "log_performance_metrics"], [34, 2, 1, "", "log_plotly_figure"], [34, 2, 1, "", "log_quantitative_analysis"], [34, 2, 1, "", "log_reference"], [34, 2, 1, "", "log_regulation"], [34, 2, 1, "", "log_risk"], [34, 2, 1, "", "log_use_case"], [34, 2, 1, "", "log_user"], [34, 2, 1, "", "log_version"]], "cyclops.tasks": [[35, 0, 0, "-", "classification"]], "cyclops.tasks.classification": [[36, 1, 1, "", "BinaryTabularClassificationTask"], [37, 1, 1, "", "MultilabelImageClassificationTask"]], "cyclops.tasks.classification.BinaryTabularClassificationTask": [[36, 2, 1, "", "__init__"], [36, 2, 1, "", "add_model"], [36, 5, 1, "", "data_type"], [36, 2, 1, "", "evaluate"], [36, 2, 1, "", "get_model"], [36, 2, 1, "", "list_models"], [36, 2, 1, "", "list_models_params"], [36, 2, 1, "", "load_model"], [36, 5, 1, "", "models_count"], [36, 2, 1, "", "predict"], [36, 2, 1, "", "save_model"], [36, 5, 1, "", "task_type"], [36, 2, 1, "", "train"]], "cyclops.tasks.classification.MultilabelImageClassificationTask": [[37, 2, 1, "", "__init__"], [37, 2, 1, "", "add_model"], [37, 5, 1, "", "data_type"], [37, 2, 1, "", "evaluate"], [37, 2, 1, "", "get_model"], [37, 2, 1, "", "list_models"], [37, 2, 1, "", "list_models_params"], [37, 2, 1, "", "load_model"], [37, 5, 1, "", "models_count"], [37, 2, 1, "", "predict"], [37, 2, 1, "", "save_model"], [37, 5, 1, "", "task_type"]]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:method", "3": "py:attribute", "4": "py:function", "5": "py:property"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "method", "Python method"], "3": ["py", "attribute", "Python attribute"], "4": ["py", "function", "Python function"], "5": ["py", "property", "Python property"]}, "titleterms": {"api": [0, 58], "refer": 0, "contribut": [1, 2], "cyclop": [1, 4, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "pre": 1, "commit": 1, "hook": 1, "code": 1, "guidelin": 1, "develop": 2, "us": [2, 5, 59], "poetri": 2, "evalu": [3, 42, 43, 44], "kei": 3, "capabl": 3, "what": 4, "i": 4, "quick": 4, "start": 4, "instal": 5, "pip": 5, "model": 6, "report": [6, 33, 34, 40], "overview": [6, 7], "dataset": 6, "quantit": 6, "analysi": 6, "fair": [6, 43], "detail": 6, "paramet": 6, "consider": 6, "monitor": [7, 24, 25, 26, 27, 28, 29, 30, 31, 32, 39, 58], "perform": 7, "subgroup": 7, "data": [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 38, 59], "aggreg": [8, 9, 10, 11], "tabular_as_aggreg": 10, "timestamp_ffill_agg": 11, "featur": [12, 13, 38], "medical_imag": [12, 13], "medicalimag": 13, "slicer": [14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "slicespec": 15, "compound_filt": 16, "filter_datetim": 17, "filter_non_nul": 18, "filter_rang": 19, "filter_string_contain": 20, "filter_valu": 21, "is_datetim": 22, "overal": 23, "clinical_appl": [24, 25], "clinicalshiftappl": 25, "synthetic_appl": [26, 27, 28, 29, 30, 31, 32], "syntheticshiftappl": 27, "binary_noise_shift": 28, "feature_association_shift": 29, "feature_swap_shift": 30, "gaussian_noise_shift": 31, "knockout_shift": 32, "modelcardreport": 34, "task": [35, 36, 37, 41], "classif": [35, 36, 37, 59], "binarytabularclassificationtask": 36, "multilabelimageclassificationtask": 37, "metric": 44, "accuraci": 45, "modul": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "interfac": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "binaryaccuraci": 45, "multiclassaccuraci": 45, "multilabelaccuraci": 45, "function": [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56], "binary_accuraci": 45, "multiclass_accuraci": 45, "multilabel_accuraci": 45, "auroc": 46, "binaryauroc": 46, "multiclassauroc": 46, "multilabelauroc": 46, "binary_auroc": 46, "multiclass_auroc": 46, "multilabel_auroc": 46, "averag": 47, "precis": [47, 50], "binaryaverageprecis": 47, "average_precis": 47, "binary_average_precis": 47, "f1": 48, "score": [48, 49], "f1score": 48, "binaryf1scor": 48, "multiclassf1scor": 48, "multilabelf1scor": 48, "f1_score": 48, "binary_f1_scor": 48, "multiclass_f1_scor": 48, "multilabel_f1_scor": 48, "f": 49, "beta": 49, "fbetascor": 49, "binaryfbetascor": 49, "multiclassfbetascor": 49, "multilabelfbetascor": 49, "fbeta_scor": 49, "binary_fbeta_scor": 49, "multiclass_fbeta_scor": 49, "multilabel_fbeta_scor": 49, "binaryprecis": 50, "multiclassprecis": 50, "multilabelprecis": 50, "binary_precis": 50, "multiclass_precis": 50, "multilabel_precis": 50, "precisionrecallcurv": 51, "binaryprecisionrecallcurv": 51, "multiclassprecisionrecallcurv": 51, "multilabelprecisionrecallcurv": 51, "precision_recall_curv": 51, "multiclass_precision_recall_curv": 51, "multilabel_precision_recall_curv": 51, "recal": 52, "binaryrecal": 52, "multiclassrecal": 52, "multilabelrecal": 52, "multiclass_recal": 52, "multilabel_recal": 52, "roc": 53, "curv": 53, "roccurv": 53, "binaryroccurv": 53, "multiclassroccurv": 53, "multilabelroccurv": 53, "roc_curv": 53, "binary_roc_curv": 53, "multiclass_roc_curv": 53, "multilabel_roc_curv": 53, "sensit": 54, "binarysensit": 54, "multiclasssensit": 54, "multilabelsensit": 54, "binary_sensit": 54, "multiclass_sensit": 54, "multilabel_sensit": 54, "specif": 55, "binaryspecif": 55, "multiclassspecif": 55, "multilabelspecif": 55, "binary_specif": 55, "multiclass_specif": 55, "multilabel_specif": 55, "statscor": 56, "binarystatscor": 56, "multiclassstatscor": 56, "multilabelstatscor": 56, "stat_scor": 56, "binary_stat_scor": 56, "multiclass_stat_scor": 56, "multilabel_stat_scor": 56, "tutori": 57, "exampl": 59, "case": 59, "tabular": 59, "kaggl": 59, "heart": 59, "failur": 59, "predict": 59, "mimiciv": 59, "mortal": 59, "synthea": 59, "prolong": 59, "length": 59, "stai": 59, "diabet": 59, "130": 59, "u": 59, "hospit": 59, "year": 59, "1999": 59, "2008": 59, "readmiss": 59, "imag": 59, "nih": 59, "chest": 59, "x": 59, "rai": 59, "user": 60, "guid": 60}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.todo": 2, "sphinx.ext.viewcode": 1, "sphinx.ext.intersphinx": 1, "sphinx": 60}, "alltitles": {"API Reference": [[0, "api-reference"]], "Contributing to cyclops": [[1, "contributing-to-cyclops"]], "Pre-commit hooks": [[1, "pre-commit-hooks"]], "Coding guidelines": [[1, "coding-guidelines"]], "\ud83e\uddd1\ud83c\udfff\u200d\ud83d\udcbb Developing": [[2, "developing"]], "Using poetry": [[2, "using-poetry"]], "Contributing": [[2, "contributing"]], "Evaluation": [[3, "evaluation"]], "Key capabilities": [[3, "key-capabilities"]], "What is CyclOps?": [[4, "what-is-cyclops"]], "Quick Start": [[4, "quick-start"]], "Installation": [[5, "installation"]], "Using pip": [[5, "using-pip"]], "Model Report": [[6, "model-report"]], "Overview": [[6, "overview"]], "Dataset": [[6, "dataset"]], "Quantitative Analysis": [[6, "quantitative-analysis"]], "Fairness Analysis": [[6, "fairness-analysis"]], "Model Details": [[6, "model-details"]], "Model Parameters": [[6, "model-parameters"]], "Considerations": [[6, "considerations"]], "Monitoring": [[7, "monitoring"]], "Overview Performance": [[7, "overview-performance"]], "Subgroup Performance": [[7, "subgroup-performance"]], "cyclops.data.aggregate": [[8, "module-cyclops.data.aggregate"]], "cyclops.data.aggregate.Aggregator": [[9, "cyclops-data-aggregate-aggregator"]], "cyclops.data.aggregate.tabular_as_aggregated": [[10, "cyclops-data-aggregate-tabular-as-aggregated"]], "cyclops.data.aggregate.timestamp_ffill_agg": [[11, "cyclops-data-aggregate-timestamp-ffill-agg"]], "cyclops.data.features.medical_image": [[12, "module-cyclops.data.features.medical_image"]], "cyclops.data.features.medical_image.MedicalImage": [[13, "cyclops-data-features-medical-image-medicalimage"]], "cyclops.data.slicer": [[14, "module-cyclops.data.slicer"]], "cyclops.data.slicer.SliceSpec": [[15, "cyclops-data-slicer-slicespec"]], "cyclops.data.slicer.compound_filter": [[16, "cyclops-data-slicer-compound-filter"]], "cyclops.data.slicer.filter_datetime": [[17, "cyclops-data-slicer-filter-datetime"]], "cyclops.data.slicer.filter_non_null": [[18, "cyclops-data-slicer-filter-non-null"]], "cyclops.data.slicer.filter_range": [[19, "cyclops-data-slicer-filter-range"]], "cyclops.data.slicer.filter_string_contains": [[20, "cyclops-data-slicer-filter-string-contains"]], "cyclops.data.slicer.filter_value": [[21, "cyclops-data-slicer-filter-value"]], "cyclops.data.slicer.is_datetime": [[22, "cyclops-data-slicer-is-datetime"]], "cyclops.data.slicer.overall": [[23, "cyclops-data-slicer-overall"]], "cyclops.monitor.clinical_applicator": [[24, "module-cyclops.monitor.clinical_applicator"]], "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator": [[25, "cyclops-monitor-clinical-applicator-clinicalshiftapplicator"]], "cyclops.monitor.synthetic_applicator": [[26, "module-cyclops.monitor.synthetic_applicator"]], "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator": [[27, "cyclops-monitor-synthetic-applicator-syntheticshiftapplicator"]], "cyclops.monitor.synthetic_applicator.binary_noise_shift": [[28, "cyclops-monitor-synthetic-applicator-binary-noise-shift"]], "cyclops.monitor.synthetic_applicator.feature_association_shift": [[29, "cyclops-monitor-synthetic-applicator-feature-association-shift"]], "cyclops.monitor.synthetic_applicator.feature_swap_shift": [[30, "cyclops-monitor-synthetic-applicator-feature-swap-shift"]], "cyclops.monitor.synthetic_applicator.gaussian_noise_shift": [[31, "cyclops-monitor-synthetic-applicator-gaussian-noise-shift"]], "cyclops.monitor.synthetic_applicator.knockout_shift": [[32, "cyclops-monitor-synthetic-applicator-knockout-shift"]], "cyclops.report.report": [[33, "module-cyclops.report.report"]], "cyclops.report.report.ModelCardReport": [[34, "cyclops-report-report-modelcardreport"]], "cyclops.tasks.classification": [[35, "module-cyclops.tasks.classification"]], "cyclops.tasks.classification.BinaryTabularClassificationTask": [[36, "cyclops-tasks-classification-binarytabularclassificationtask"]], "cyclops.tasks.classification.MultilabelImageClassificationTask": [[37, "cyclops-tasks-classification-multilabelimageclassificationtask"]], "cyclops.data": [[38, "module-cyclops.data"]], "cyclops.data.features": [[38, "module-cyclops.data.features"]], "cyclops.monitor": [[39, "module-cyclops.monitor"]], "cyclops.report": [[40, "module-cyclops.report"]], "cyclops.tasks": [[41, "module-cyclops.tasks"]], "Evaluator": [[42, "evaluator"]], "Fairness Evaluator": [[43, "fairness-evaluator"]], "Evaluation Metrics": [[44, "evaluation-metrics"]], "Accuracy": [[45, "accuracy"], [45, "id1"]], "Module Interface": [[45, "module-interface"], [46, "module-interface"], [47, "module-interface"], [48, "module-interface"], [49, "module-interface"], [50, "module-interface"], [51, "module-interface"], [52, "module-interface"], [53, "module-interface"], [54, "module-interface"], [55, "module-interface"], [56, "module-interface"]], "BinaryAccuracy": [[45, "binaryaccuracy"]], "MulticlassAccuracy": [[45, "multiclassaccuracy"]], "MultilabelAccuracy": [[45, "multilabelaccuracy"]], "Functional Interface": [[45, "functional-interface"], [46, "functional-interface"], [47, "functional-interface"], [48, "functional-interface"], [49, "functional-interface"], [50, "functional-interface"], [51, "functional-interface"], [52, "functional-interface"], [53, "functional-interface"], [54, "functional-interface"], [55, "functional-interface"], [56, "functional-interface"]], "binary_accuracy": [[45, "binary-accuracy"]], "multiclass_accuracy": [[45, "multiclass-accuracy"]], "multilabel_accuracy": [[45, "multilabel-accuracy"]], "AUROC": [[46, "auroc"], [46, "id1"]], "BinaryAUROC": [[46, "binaryauroc"]], "MulticlassAUROC": [[46, "multiclassauroc"]], "MultilabelAUROC": [[46, "multilabelauroc"]], "auroc": [[46, "id2"]], "binary_auroc": [[46, "binary-auroc"]], "multiclass_auroc": [[46, "multiclass-auroc"]], "multilabel_auroc": [[46, "multilabel-auroc"]], "Average Precision": [[47, "average-precision"]], "BinaryAveragePrecision": [[47, "binaryaverageprecision"]], "average_precision": [[47, "id1"]], "binary_average_precision": [[47, "binary-average-precision"]], "F1-Score": [[48, "f1-score"]], "F1Score": [[48, "f1score"]], "BinaryF1Score": [[48, "binaryf1score"]], "MulticlassF1Score": [[48, "multiclassf1score"]], "MultilabelF1Score": [[48, "multilabelf1score"]], "f1_score": [[48, "id1"]], "binary_f1_score": [[48, "binary-f1-score"]], "multiclass_f1_score": [[48, "multiclass-f1-score"]], "multilabel_f1_score": [[48, "multilabel-f1-score"]], "F-beta Score": [[49, "f-beta-score"]], "FbetaScore": [[49, "fbetascore"]], "BinaryFbetaScore": [[49, "binaryfbetascore"]], "MulticlassFbetaScore": [[49, "multiclassfbetascore"]], "MultilabelFbetaScore": [[49, "multilabelfbetascore"]], "fbeta_score": [[49, "fbeta-score"]], "binary_fbeta_score": [[49, "binary-fbeta-score"]], "multiclass_fbeta_score": [[49, "multiclass-fbeta-score"]], "multilabel_fbeta_score": [[49, "multilabel-fbeta-score"]], "Precision": [[50, "precision"], [50, "id1"]], "BinaryPrecision": [[50, "binaryprecision"]], "MulticlassPrecision": [[50, "multiclassprecision"]], "MultilabelPrecision": [[50, "multilabelprecision"]], "precision": [[50, "id2"]], "binary_precision": [[50, "binary-precision"]], "multiclass_precision": [[50, "multiclass-precision"]], "multilabel_precision": [[50, "multilabel-precision"]], "PrecisionRecallCurve": [[51, "precisionrecallcurve"], [51, "id1"]], "BinaryPrecisionRecallCurve": [[51, "binaryprecisionrecallcurve"]], "MulticlassPrecisionRecallCurve": [[51, "multiclassprecisionrecallcurve"]], "MultilabelPrecisionRecallCurve": [[51, "multilabelprecisionrecallcurve"]], "precision_recall_curve": [[51, "precision-recall-curve"]], "multiclass_precision_recall_curve": [[51, "multiclass-precision-recall-curve"]], "multilabel_precision_recall_curve": [[51, "multilabel-precision-recall-curve"]], "Recall": [[52, "recall"], [52, "id1"]], "BinaryRecall": [[52, "binaryrecall"]], "MulticlassRecall": [[52, "multiclassrecall"]], "MultilabelRecall": [[52, "multilabelrecall"]], "recall": [[52, "id2"]], "multiclass_recall": [[52, "multiclass-recall"]], "multilabel_recall": [[52, "multilabel-recall"]], "ROC Curve": [[53, "roc-curve"]], "ROCCurve": [[53, "roccurve"]], "BinaryROCCurve": [[53, "binaryroccurve"]], "MulticlassROCCurve": [[53, "multiclassroccurve"]], "MultilabelROCCurve": [[53, "multilabelroccurve"]], "roc_curve": [[53, "id1"]], "binary_roc_curve": [[53, "binary-roc-curve"]], "multiclass_roc_curve": [[53, "multiclass-roc-curve"]], "multilabel_roc_curve": [[53, "multilabel-roc-curve"]], "Sensitivity": [[54, "sensitivity"], [54, "id1"]], "BinarySensitivity": [[54, "binarysensitivity"]], "MulticlassSensitivity": [[54, "multiclasssensitivity"]], "MultilabelSensitivity": [[54, "multilabelsensitivity"]], "sensitivity": [[54, "id2"]], "binary_sensitivity": [[54, "binary-sensitivity"]], "multiclass_sensitivity": [[54, "multiclass-sensitivity"]], "multilabel_sensitivity": [[54, "multilabel-sensitivity"]], "Specificity": [[55, "specificity"], [55, "id1"]], "BinarySpecificity": [[55, "binaryspecificity"]], "MulticlassSpecificity": [[55, "multiclassspecificity"]], "MultilabelSpecificity": [[55, "multilabelspecificity"]], "specificity": [[55, "id2"]], "binary_specificity": [[55, "binary-specificity"]], "multiclass_specificity": [[55, "multiclass-specificity"]], "multilabel_specificity": [[55, "multilabel-specificity"]], "StatScores": [[56, "statscores"], [56, "id1"]], "BinaryStatScores": [[56, "binarystatscores"]], "MulticlassStatScores": [[56, "multiclassstatscores"]], "MultilabelStatScores": [[56, "multilabelstatscores"]], "stat_scores": [[56, "stat-scores"]], "binary_stat_scores": [[56, "binary-stat-scores"]], "multiclass_stat_scores": [[56, "multiclass-stat-scores"]], "multilabel_stat_scores": [[56, "multilabel-stat-scores"]], "Tutorials": [[57, "tutorials"]], "monitor API": [[58, "monitor-api"]], "Example use cases": [[59, "example-use-cases"]], "Tabular data": [[59, "tabular-data"]], "Kaggle Heart Failure Prediction": [[59, "kaggle-heart-failure-prediction"]], "MIMICIV Mortality Prediction": [[59, "mimiciv-mortality-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[59, "synthea-prolonged-length-of-stay-prediction"]], "Diabetes 130-US Hospitals for Years 1999-2008 Readmission Prediction": [[59, "diabetes-130-us-hospitals-for-years-1999-2008-readmission-prediction"]], "Image data": [[59, "image-data"]], "NIH Chest X-ray classification": [[59, "nih-chest-x-ray-classification"]], "User Guide": [[60, "user-guide"]]}, "indexentries": {"cyclops.data.aggregate": [[8, "module-cyclops.data.aggregate"]], "module": [[8, "module-cyclops.data.aggregate"], [12, "module-cyclops.data.features.medical_image"], [14, "module-cyclops.data.slicer"], [24, "module-cyclops.monitor.clinical_applicator"], [26, "module-cyclops.monitor.synthetic_applicator"], [33, "module-cyclops.report.report"], [35, "module-cyclops.tasks.classification"], [38, "module-cyclops.data"], [38, "module-cyclops.data.features"], [39, "module-cyclops.monitor"], [40, "module-cyclops.report"], [41, "module-cyclops.tasks"]], "aggregator (class in cyclops.data.aggregate)": [[9, "cyclops.data.aggregate.Aggregator"]], "__init__() (aggregator method)": [[9, "cyclops.data.aggregate.Aggregator.__init__"]], "agg_by (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.agg_by"]], "agg_meta_for (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.agg_meta_for"]], "aggfuncs (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.aggfuncs"]], "fit() (aggregator method)": [[9, "cyclops.data.aggregate.Aggregator.fit"]], "fit_transform() (aggregator method)": [[9, "cyclops.data.aggregate.Aggregator.fit_transform"]], "imputer (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.imputer"]], "num_timesteps (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.num_timesteps"]], "set_output() (aggregator method)": [[9, "cyclops.data.aggregate.Aggregator.set_output"]], "time_by (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.time_by"]], "timestamp_col (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.timestamp_col"]], "timestep_size (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.timestep_size"]], "transform() (aggregator method)": [[9, "cyclops.data.aggregate.Aggregator.transform"]], "window_duration (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.window_duration"]], "window_start_time (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.window_start_time"]], "window_stop_time (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.window_stop_time"]], "window_times (aggregator attribute)": [[9, "cyclops.data.aggregate.Aggregator.window_times"]], "tabular_as_aggregated() (in module cyclops.data.aggregate)": [[10, "cyclops.data.aggregate.tabular_as_aggregated"]], "timestamp_ffill_agg() (in module cyclops.data.aggregate)": [[11, "cyclops.data.aggregate.timestamp_ffill_agg"]], "cyclops.data.features.medical_image": [[12, "module-cyclops.data.features.medical_image"]], "medicalimage (class in cyclops.data.features.medical_image)": [[13, "cyclops.data.features.medical_image.MedicalImage"]], "__call__() (medicalimage method)": [[13, "cyclops.data.features.medical_image.MedicalImage.__call__"]], "cast_storage() (medicalimage method)": [[13, "cyclops.data.features.medical_image.MedicalImage.cast_storage"]], "decode_example() (medicalimage method)": [[13, "cyclops.data.features.medical_image.MedicalImage.decode_example"]], "embed_storage() (medicalimage method)": [[13, "cyclops.data.features.medical_image.MedicalImage.embed_storage"]], "encode_example() (medicalimage method)": [[13, "cyclops.data.features.medical_image.MedicalImage.encode_example"]], "flatten() (medicalimage method)": [[13, "cyclops.data.features.medical_image.MedicalImage.flatten"]], "cyclops.data.slicer": [[14, "module-cyclops.data.slicer"]], "slicespec (class in cyclops.data.slicer)": [[15, "cyclops.data.slicer.SliceSpec"]], "_registry (slicespec attribute)": [[15, "cyclops.data.slicer.SliceSpec._registry"]], "add_slice_spec() (slicespec method)": [[15, "cyclops.data.slicer.SliceSpec.add_slice_spec"]], "column_names (slicespec attribute)": [[15, "cyclops.data.slicer.SliceSpec.column_names"]], "get_slices() (slicespec method)": [[15, "cyclops.data.slicer.SliceSpec.get_slices"]], "include_overall (slicespec attribute)": [[15, "cyclops.data.slicer.SliceSpec.include_overall"]], "slices() (slicespec method)": [[15, "cyclops.data.slicer.SliceSpec.slices"]], "spec_list (slicespec attribute)": [[15, "cyclops.data.slicer.SliceSpec.spec_list"]], "validate (slicespec attribute)": [[15, "cyclops.data.slicer.SliceSpec.validate"]], "compound_filter() (in module cyclops.data.slicer)": [[16, "cyclops.data.slicer.compound_filter"]], "filter_datetime() (in module cyclops.data.slicer)": [[17, "cyclops.data.slicer.filter_datetime"]], "filter_non_null() (in module cyclops.data.slicer)": [[18, "cyclops.data.slicer.filter_non_null"]], "filter_range() (in module cyclops.data.slicer)": [[19, "cyclops.data.slicer.filter_range"]], "filter_string_contains() (in module cyclops.data.slicer)": [[20, "cyclops.data.slicer.filter_string_contains"]], "filter_value() (in module cyclops.data.slicer)": [[21, "cyclops.data.slicer.filter_value"]], "is_datetime() (in module cyclops.data.slicer)": [[22, "cyclops.data.slicer.is_datetime"]], "overall() (in module cyclops.data.slicer)": [[23, "cyclops.data.slicer.overall"]], "cyclops.monitor.clinical_applicator": [[24, "module-cyclops.monitor.clinical_applicator"]], "clinicalshiftapplicator (class in cyclops.monitor.clinical_applicator)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator"]], "age() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.age"]], "apply_shift() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.apply_shift"]], "custom() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.custom"]], "hospital_type() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.hospital_type"]], "month() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.month"]], "sex() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.sex"]], "time() (clinicalshiftapplicator method)": [[25, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.time"]], "cyclops.monitor.synthetic_applicator": [[26, "module-cyclops.monitor.synthetic_applicator"]], "syntheticshiftapplicator (class in cyclops.monitor.synthetic_applicator)": [[27, "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator"]], "apply_shift() (syntheticshiftapplicator method)": [[27, "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.apply_shift"]], "binary_noise_shift() (in module cyclops.monitor.synthetic_applicator)": [[28, "cyclops.monitor.synthetic_applicator.binary_noise_shift"]], "feature_association_shift() (in module cyclops.monitor.synthetic_applicator)": [[29, "cyclops.monitor.synthetic_applicator.feature_association_shift"]], "feature_swap_shift() (in module cyclops.monitor.synthetic_applicator)": [[30, "cyclops.monitor.synthetic_applicator.feature_swap_shift"]], "gaussian_noise_shift() (in module cyclops.monitor.synthetic_applicator)": [[31, "cyclops.monitor.synthetic_applicator.gaussian_noise_shift"]], "knockout_shift() (in module cyclops.monitor.synthetic_applicator)": [[32, "cyclops.monitor.synthetic_applicator.knockout_shift"]], "cyclops.report.report": [[33, "module-cyclops.report.report"]], "modelcardreport (class in cyclops.report.report)": [[34, "cyclops.report.report.ModelCardReport"]], "export() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.export"]], "from_json_file() (modelcardreport class method)": [[34, "cyclops.report.report.ModelCardReport.from_json_file"]], "log_citation() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_citation"]], "log_dataset() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_dataset"]], "log_descriptor() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_descriptor"]], "log_fairness_assessment() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_fairness_assessment"]], "log_from_dict() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_from_dict"]], "log_image() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_image"]], "log_license() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_license"]], "log_model_parameters() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_model_parameters"]], "log_owner() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_owner"]], "log_performance_metrics() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_performance_metrics"]], "log_plotly_figure() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_plotly_figure"]], "log_quantitative_analysis() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_quantitative_analysis"]], "log_reference() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_reference"]], "log_regulation() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_regulation"]], "log_risk() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_risk"]], "log_use_case() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_use_case"]], "log_user() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_user"]], "log_version() (modelcardreport method)": [[34, "cyclops.report.report.ModelCardReport.log_version"]], "cyclops.tasks.classification": [[35, "module-cyclops.tasks.classification"]], "binarytabularclassificationtask (class in cyclops.tasks.classification)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask"]], "__init__() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.__init__"]], "add_model() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.add_model"]], "data_type (binarytabularclassificationtask property)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.data_type"]], "evaluate() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.evaluate"]], "get_model() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.get_model"]], "list_models() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.list_models"]], "list_models_params() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.list_models_params"]], "load_model() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.load_model"]], "models_count (binarytabularclassificationtask property)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.models_count"]], "predict() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.predict"]], "save_model() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.save_model"]], "task_type (binarytabularclassificationtask property)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.task_type"]], "train() (binarytabularclassificationtask method)": [[36, "cyclops.tasks.classification.BinaryTabularClassificationTask.train"]], "multilabelimageclassificationtask (class in cyclops.tasks.classification)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask"]], "__init__() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.__init__"]], "add_model() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.add_model"]], "data_type (multilabelimageclassificationtask property)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.data_type"]], "evaluate() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.evaluate"]], "get_model() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.get_model"]], "list_models() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.list_models"]], "list_models_params() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.list_models_params"]], "load_model() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.load_model"]], "models_count (multilabelimageclassificationtask property)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.models_count"]], "predict() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.predict"]], "save_model() (multilabelimageclassificationtask method)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.save_model"]], "task_type (multilabelimageclassificationtask property)": [[37, "cyclops.tasks.classification.MultilabelImageClassificationTask.task_type"]], "cyclops.data": [[38, "module-cyclops.data"]], "cyclops.data.features": [[38, "module-cyclops.data.features"]], "cyclops.monitor": [[39, "module-cyclops.monitor"]], "cyclops.report": [[40, "module-cyclops.report"]], "cyclops.tasks": [[41, "module-cyclops.tasks"]], "evaluate() (in module cyclops.evaluate.evaluator)": [[42, "cyclops.evaluate.evaluator.evaluate"]], "evaluate_fairness() (in module cyclops.evaluate.fairness.evaluator)": [[43, "cyclops.evaluate.fairness.evaluator.evaluate_fairness"]], "accuracy (class in cyclops.evaluate.metrics)": [[45, "cyclops.evaluate.metrics.Accuracy"]], "binaryaccuracy (class in cyclops.evaluate.metrics)": [[45, "cyclops.evaluate.metrics.BinaryAccuracy"]], "multiclassaccuracy (class in cyclops.evaluate.metrics)": [[45, "cyclops.evaluate.metrics.MulticlassAccuracy"]], "multilabelaccuracy (class in cyclops.evaluate.metrics)": [[45, "cyclops.evaluate.metrics.MultilabelAccuracy"]], "binary_accuracy() (in module cyclops.evaluate.metrics.functional.accuracy)": [[45, "cyclops.evaluate.metrics.functional.accuracy.binary_accuracy"]], "multiclass_accuracy() (in module cyclops.evaluate.metrics.functional.accuracy)": [[45, "cyclops.evaluate.metrics.functional.accuracy.multiclass_accuracy"]], "multilabel_accuracy() (in module cyclops.evaluate.metrics.functional.accuracy)": [[45, "cyclops.evaluate.metrics.functional.accuracy.multilabel_accuracy"]], "auroc (class in cyclops.evaluate.metrics)": [[46, "cyclops.evaluate.metrics.AUROC"]], "binaryauroc (class in cyclops.evaluate.metrics)": [[46, "cyclops.evaluate.metrics.BinaryAUROC"]], "multiclassauroc (class in cyclops.evaluate.metrics)": [[46, "cyclops.evaluate.metrics.MulticlassAUROC"]], "multilabelauroc (class in cyclops.evaluate.metrics)": [[46, "cyclops.evaluate.metrics.MultilabelAUROC"]], "auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[46, "cyclops.evaluate.metrics.functional.auroc.auroc"]], "binary_auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[46, "cyclops.evaluate.metrics.functional.auroc.binary_auroc"]], "multiclass_auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[46, "cyclops.evaluate.metrics.functional.auroc.multiclass_auroc"]], "multilabel_auroc() (in module cyclops.evaluate.metrics.functional.auroc)": [[46, "cyclops.evaluate.metrics.functional.auroc.multilabel_auroc"]], "binaryaverageprecision (class in cyclops.evaluate.metrics)": [[47, "cyclops.evaluate.metrics.BinaryAveragePrecision"]], "average_precision() (in module cyclops.evaluate.metrics.functional.average_precision)": [[47, "cyclops.evaluate.metrics.functional.average_precision.average_precision"]], "binary_average_precision() (in module cyclops.evaluate.metrics.functional.average_precision)": [[47, "cyclops.evaluate.metrics.functional.average_precision.binary_average_precision"]], "binaryf1score (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.BinaryF1Score"]], "f1score (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.F1Score"]], "multiclassf1score (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.MulticlassF1Score"]], "multilabelf1score (class in cyclops.evaluate.metrics)": [[48, "cyclops.evaluate.metrics.MultilabelF1Score"]], "binary_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[48, "cyclops.evaluate.metrics.functional.f_beta.binary_f1_score"]], "f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[48, "cyclops.evaluate.metrics.functional.f_beta.f1_score"]], "multiclass_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[48, "cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score"]], "multilabel_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[48, "cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score"]], "binaryfbetascore (class in cyclops.evaluate.metrics)": [[49, "cyclops.evaluate.metrics.BinaryFbetaScore"]], "fbetascore (class in cyclops.evaluate.metrics)": [[49, "cyclops.evaluate.metrics.FbetaScore"]], "multiclassfbetascore (class in cyclops.evaluate.metrics)": [[49, "cyclops.evaluate.metrics.MulticlassFbetaScore"]], "multilabelfbetascore (class in cyclops.evaluate.metrics)": [[49, "cyclops.evaluate.metrics.MultilabelFbetaScore"]], "binary_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[49, "cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score"]], "fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[49, "cyclops.evaluate.metrics.functional.f_beta.fbeta_score"]], "multiclass_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[49, "cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score"]], "multilabel_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[49, "cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score"]], "binaryprecision (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.BinaryPrecision"]], "multiclassprecision (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.MulticlassPrecision"]], "multilabelprecision (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.MultilabelPrecision"]], "precision (class in cyclops.evaluate.metrics)": [[50, "cyclops.evaluate.metrics.Precision"]], "binary_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[50, "cyclops.evaluate.metrics.functional.precision_recall.binary_precision"]], "multiclass_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[50, "cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision"]], "multilabel_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[50, "cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision"]], "precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[50, "cyclops.evaluate.metrics.functional.precision_recall.precision"]], "binaryprecisionrecallcurve (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.BinaryPrecisionRecallCurve"]], "multiclassprecisionrecallcurve (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.MulticlassPrecisionRecallCurve"]], "multilabelprecisionrecallcurve (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.MultilabelPrecisionRecallCurve"]], "precisionrecallcurve (class in cyclops.evaluate.metrics)": [[51, "cyclops.evaluate.metrics.PrecisionRecallCurve"]], "multiclass_precision_recall_curve() (in module cyclops.evaluate.metrics.functional.precision_recall_curve)": [[51, "cyclops.evaluate.metrics.functional.precision_recall_curve.multiclass_precision_recall_curve"]], "multilabel_precision_recall_curve() (in module cyclops.evaluate.metrics.functional.precision_recall_curve)": [[51, "cyclops.evaluate.metrics.functional.precision_recall_curve.multilabel_precision_recall_curve"]], "precision_recall_curve() (in module cyclops.evaluate.metrics.functional.precision_recall_curve)": [[51, "cyclops.evaluate.metrics.functional.precision_recall_curve.precision_recall_curve"]], "binaryrecall (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.BinaryRecall"]], "multiclassrecall (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.MulticlassRecall"]], "multilabelrecall (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.MultilabelRecall"]], "recall (class in cyclops.evaluate.metrics)": [[52, "cyclops.evaluate.metrics.Recall"]], "multiclass_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[52, "cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall"]], "multilabel_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[52, "cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall"]], "recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[52, "cyclops.evaluate.metrics.functional.precision_recall.recall"]], "binaryroccurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.BinaryROCCurve"]], "multiclassroccurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.MulticlassROCCurve"]], "multilabelroccurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.MultilabelROCCurve"]], "roccurve (class in cyclops.evaluate.metrics)": [[53, "cyclops.evaluate.metrics.ROCCurve"]], "binary_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[53, "cyclops.evaluate.metrics.functional.roc.binary_roc_curve"]], "multiclass_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[53, "cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve"]], "multilabel_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[53, "cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve"]], "roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[53, "cyclops.evaluate.metrics.functional.roc.roc_curve"]], "binarysensitivity (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.BinarySensitivity"]], "multiclasssensitivity (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.MulticlassSensitivity"]], "multilabelsensitivity (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.MultilabelSensitivity"]], "sensitivity (class in cyclops.evaluate.metrics)": [[54, "cyclops.evaluate.metrics.Sensitivity"]], "binary_sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[54, "cyclops.evaluate.metrics.functional.sensitivity.binary_sensitivity"]], "multiclass_sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[54, "cyclops.evaluate.metrics.functional.sensitivity.multiclass_sensitivity"]], "multilabel_sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[54, "cyclops.evaluate.metrics.functional.sensitivity.multilabel_sensitivity"]], "sensitivity() (in module cyclops.evaluate.metrics.functional.sensitivity)": [[54, "cyclops.evaluate.metrics.functional.sensitivity.sensitivity"]], "binaryspecificity (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.BinarySpecificity"]], "multiclassspecificity (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.MulticlassSpecificity"]], "multilabelspecificity (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.MultilabelSpecificity"]], "specificity (class in cyclops.evaluate.metrics)": [[55, "cyclops.evaluate.metrics.Specificity"]], "binary_specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[55, "cyclops.evaluate.metrics.functional.specificity.binary_specificity"]], "multiclass_specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[55, "cyclops.evaluate.metrics.functional.specificity.multiclass_specificity"]], "multilabel_specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[55, "cyclops.evaluate.metrics.functional.specificity.multilabel_specificity"]], "specificity() (in module cyclops.evaluate.metrics.functional.specificity)": [[55, "cyclops.evaluate.metrics.functional.specificity.specificity"]], "binarystatscores (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.BinaryStatScores"]], "multiclassstatscores (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.MulticlassStatScores"]], "multilabelstatscores (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.MultilabelStatScores"]], "statscores (class in cyclops.evaluate.metrics)": [[56, "cyclops.evaluate.metrics.StatScores"]], "binary_stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[56, "cyclops.evaluate.metrics.functional.stat_scores.binary_stat_scores"]], "multiclass_stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[56, "cyclops.evaluate.metrics.functional.stat_scores.multiclass_stat_scores"]], "multilabel_stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[56, "cyclops.evaluate.metrics.functional.stat_scores.multilabel_stat_scores"]], "stat_scores() (in module cyclops.evaluate.metrics.functional.stat_scores)": [[56, "cyclops.evaluate.metrics.functional.stat_scores.stat_scores"]]}})
\ No newline at end of file
diff --git a/api/tutorials.html b/api/tutorials.html
index 9ec564fa6..d8b73a118 100644
--- a/api/tutorials.html
+++ b/api/tutorials.html
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current has-children current-page"><a class="current reference internal" href="#">Tutorials</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
diff --git a/api/tutorials/diabetes_130/readmission_prediction.html b/api/tutorials/diabetes_130/readmission_prediction.html
deleted file mode 100644
index 785d8365b..000000000
--- a/api/tutorials/diabetes_130/readmission_prediction.html
+++ /dev/null
@@ -1,1494 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en">
-  <head><meta charset="utf-8"/>
-    <meta name="viewport" content="width=device-width,initial-scale=1"/>
-    <meta name="color-scheme" content="light dark">
-    <meta name="google-site-verification" content="i0qQRaR9OA3tSz_9tDocdcXGY27Ox_cy4FrvTHD2C_0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-
-    
-      <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Chest X-Ray Disease Classification" href="../nihcxr/cxr_classification.html" /><link rel="prev" title="Prolonged Length of Stay Prediction" href="../synthea/los_prediction.html" />
-
-    <link rel="shortcut icon" href="../../_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
-
-    
-    <meta name="msvalidate.01" content="8D2A5032F006424CAF54C0DDCD16F666" />
-      
-        <title>Readmission Prediction - cyclops documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
-    <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="../../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
-    <link rel="stylesheet" type="text/css" href="../../_static/nbsphinx-code-cells.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/css/cyclops.css?v=9f20b50b" />
-    
-    
-
-
-<style>
-  body {
-    --color-code-background: #f8f8f8;
-  --color-code-foreground: black;
-  --color-card-background: #5e88f2;
-  
-  }
-  @media not print {
-    body[data-theme="dark"] {
-      --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-    }
-    @media (prefers-color-scheme: dark) {
-      body:not([data-theme="light"]) {
-        --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-      }
-    }
-  }
-</style><script src="../../_static/documentation_options.js?v=5929fcd5"></script>
-    <script src="../../_static/doctools.js?v=888ff710"></script>
-    <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="../../_static/scripts/furo.js?v=32e29ea5"></script>
-    <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
-    <script src="../../_static/copybutton.js?v=ccdb6887"></script>
-    <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-    <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script src="../../_static/require.min.js?v=53367eec"></script>
-    <script src="../../_static/custom.js?v=275847af"></script>
-    
-    <script src="https://kit.fontawesome.com/2c1f516901.js" crossorigin="anonymous"></script>
-
-  </head>
-  <body>
-    
-    <script>
-      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
-    </script>
-    
-
-<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
-  <symbol id="svg-toc" viewBox="0 0 24 24">
-    <title>Contents</title>
-    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
-      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-menu" viewBox="0 0 24 24">
-    <title>Menu</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
-      <line x1="3" y1="12" x2="21" y2="12"></line>
-      <line x1="3" y1="6" x2="21" y2="6"></line>
-      <line x1="3" y1="18" x2="21" y2="18"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
-    <title>Expand</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
-      <polyline points="9 18 15 12 9 6"></polyline>
-    </svg>
-  </symbol>
-  <symbol id="svg-sun" viewBox="0 0 24 24">
-    <title>Light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
-      <circle cx="12" cy="12" r="5"></circle>
-      <line x1="12" y1="1" x2="12" y2="3"></line>
-      <line x1="12" y1="21" x2="12" y2="23"></line>
-      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
-      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
-      <line x1="1" y1="12" x2="3" y2="12"></line>
-      <line x1="21" y1="12" x2="23" y2="12"></line>
-      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
-      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon" viewBox="0 0 24 24">
-    <title>Dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
-      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
-    </svg>
-  </symbol>
-  <symbol id="svg-sun-half" viewBox="0 0 24 24">
-    <title>Auto light/dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-      <circle cx="12" cy="12" r="9" />
-      <path d="M13 12h5" />
-      <path d="M13 15h4" />
-      <path d="M13 18h1" />
-      <path d="M13 9h4" />
-      <path d="M13 6h1" />
-    </svg>
-  </symbol>
-</svg>
-
-<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
-<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
-<label class="overlay sidebar-overlay" for="__navigation">
-  <div class="visually-hidden">Hide navigation sidebar</div>
-</label>
-<label class="overlay toc-overlay" for="__toc">
-  <div class="visually-hidden">Hide table of contents sidebar</div>
-</label>
-
-
-
-<div class="page">
-  <header class="mobile-header">
-    <div class="header-left">
-      <label class="nav-overlay-icon" for="__navigation">
-        <div class="visually-hidden">Toggle site navigation sidebar</div>
-        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
-      </label>
-    </div>
-    <div class="header-center">
-      <a href="../../index.html"><div class="brand">cyclops  documentation</div></a>
-    </div>
-    <div class="header-right">
-      <div class="theme-toggle-container theme-toggle-header">
-        <button class="theme-toggle">
-          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-        </button>
-      </div>
-      <label class="toc-overlay-icon toc-header-icon" for="__toc">
-        <div class="visually-hidden">Toggle table of contents sidebar</div>
-        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-      </label>
-    </div>
-  </header>
-  <aside class="sidebar-drawer">
-    <div class="sidebar-container">
-      
-      <div class="sidebar-sticky"><a class="sidebar-brand" href="../../index.html">
-  
-  <div class="sidebar-logo-container">
-    <img class="sidebar-logo" src="../../_static/cyclops_logo-dark.png" alt="Logo"/>
-  </div>
-  
-  
-</a><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
-  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
-  <input type="hidden" name="check_keywords" value="yes">
-  <input type="hidden" name="area" value="default">
-</form>
-<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <ul class="current">
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l2 current has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="../kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../developing.html">🧑🏿‍💻 Developing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing to cyclops</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../api.html">API Reference</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of API Reference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.data.html">cyclops.data</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of cyclops.data</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.html">cyclops.data.slicer</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of cyclops.data.slicer</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.compound_filter.html">cyclops.data.slicer.compound_filter</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html">cyclops.data.slicer.filter_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html">cyclops.data.slicer.filter_non_null</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_range.html">cyclops.data.slicer.filter_range</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html">cyclops.data.slicer.filter_string_contains</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_value.html">cyclops.data.slicer.filter_value</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.is_datetime.html">cyclops.data.slicer.is_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.overall.html">cyclops.data.slicer.overall</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html">cyclops.data.slicer.SliceSpec</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.html">cyclops.data.aggregate</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of cyclops.data.aggregate</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html">cyclops.data.aggregate.tabular_as_aggregated</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html">cyclops.data.aggregate.timestamp_ffill_agg</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html">cyclops.data.aggregate.Aggregator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.html">cyclops.data.features.medical_image</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of cyclops.data.features.medical_image</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html">cyclops.data.features.medical_image.MedicalImage</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.tasks.html">cyclops.tasks</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of cyclops.tasks</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.html">cyclops.tasks.classification</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of cyclops.tasks.classification</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html">cyclops.tasks.classification.BinaryTabularClassificationTask</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html">cyclops.tasks.classification.MultilabelImageClassificationTask</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/evaluator.html">Evaluator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/fairness_evaluator.html">Fairness Evaluator</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/metrics.html">Evaluation Metrics</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of Evaluation Metrics</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/accuracy.html">Accuracy</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/auroc.html">AUROC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/average_precision.html">Average Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f1score.html">F1-Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f_beta.html">F-beta Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision.html">Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision_recall_curve.html">PrecisionRecallCurve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/recall.html">Recall</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/roc.html">ROC Curve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/sensitivity.html">Sensitivity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/specificity.html">Specificity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/stat_scores.html">StatScores</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.monitor.html">cyclops.monitor</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of cyclops.monitor</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.html">cyclops.monitor.clinical_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of cyclops.monitor.clinical_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html">cyclops.monitor.clinical_applicator.ClinicalShiftApplicator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html">cyclops.monitor.synthetic_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of cyclops.monitor.synthetic_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html">cyclops.monitor.synthetic_applicator.binary_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html">cyclops.monitor.synthetic_applicator.feature_association_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html">cyclops.monitor.synthetic_applicator.feature_swap_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html">cyclops.monitor.synthetic_applicator.gaussian_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html">cyclops.monitor.synthetic_applicator.knockout_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html">cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.report.html">cyclops.report</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of cyclops.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.html">cyclops.report.report</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of cyclops.report.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.ModelCardReport.html">cyclops.report.report.ModelCardReport</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-</div>
-</div>
-
-      </div>
-      
-    </div>
-  </aside>
-  <div class="main">
-    <div class="content">
-      <div class="article-container">
-        <a href="#" class="back-to-top muted-link">
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
-            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
-          </svg>
-          <span>Back to top</span>
-        </a>
-        <div class="content-icon-container">
-          
-<div class="theme-toggle-container theme-toggle-content">
-            <button class="theme-toggle">
-              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-            </button>
-          </div>
-          <label class="toc-overlay-icon toc-content-icon" for="__toc">
-            <div class="visually-hidden">Toggle table of contents sidebar</div>
-            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-          </label>
-        </div>
-        <article role="main">
-          <section id="Readmission-Prediction">
-<h1>Readmission Prediction<a class="headerlink" href="#Readmission-Prediction" title="Link to this heading">#</a></h1>
-<p>This notebook showcases readmission prediction on the <a class="reference external" href="https://archive.ics.uci.edu/dataset/296/diabetes+130-us+hospitals+for+years+1999-2008">Diabetes 130-US Hospitals for Years 1999-2008</a> using CyclOps. The task is formulated as a binary classification task, where we predict the probability of early readmission of the patient within 30 days of discharge.</p>
-<section id="Install-libraries">
-<h2>Install libraries<a class="headerlink" href="#Install-libraries" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pycyclops
-<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>ucimlrepo
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Import-Libraries">
-<h2>Import Libraries<a class="headerlink" href="#Import-Libraries" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;Readmission prediction.&quot;&quot;&quot;</span>
-
-<span class="c1"># ruff: noqa: E402</span>
-
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">inspect</span>
-<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">date</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
-<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">Dataset</span>
-<span class="kn">from</span> <span class="nn">datasets.features</span> <span class="kn">import</span> <span class="n">ClassLabel</span>
-<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
-<span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
-<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">OneHotEncoder</span>
-<span class="kn">from</span> <span class="nn">ucimlrepo</span> <span class="kn">import</span> <span class="n">fetch_ucirepo</span>
-
-<span class="kn">from</span> <span class="nn">cyclops.data.df.feature</span> <span class="kn">import</span> <span class="n">TabularFeatures</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.slicer</span> <span class="kn">import</span> <span class="n">SliceSpec</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.fairness</span> <span class="kn">import</span> <span class="n">FairnessConfig</span>  <span class="c1"># noqa: E402</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics</span> <span class="kn">import</span> <span class="n">create_metric</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics.experimental.functional</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">binary_npv</span><span class="p">,</span>
-    <span class="n">binary_ppv</span><span class="p">,</span>
-    <span class="n">binary_roc</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics.experimental.metric_dict</span> <span class="kn">import</span> <span class="n">MetricDict</span>
-<span class="kn">from</span> <span class="nn">cyclops.models.catalog</span> <span class="kn">import</span> <span class="n">create_model</span>
-<span class="kn">from</span> <span class="nn">cyclops.report</span> <span class="kn">import</span> <span class="n">ModelCardReport</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.plot.classification</span> <span class="kn">import</span> <span class="n">ClassificationPlotter</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.utils</span> <span class="kn">import</span> <span class="n">flatten_results_dict</span>
-<span class="kn">from</span> <span class="nn">cyclops.tasks</span> <span class="kn">import</span> <span class="n">BinaryTabularClassificationTask</span>
-</pre></div>
-</div>
-</div>
-<p>CyclOps offers a package for documentation of the model through a model report. The <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> class is used to populate and generate the model report as an HTML file. The model report has the following sections:</p>
-<ul class="simple">
-<li><p>Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).</p></li>
-<li><p>Datasets: High level statistics of the training data, including changes in distribution over time.</p></li>
-<li><p>Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.</p></li>
-<li><p>Fairness Analysis: This section contains the fairness metrics of the model.</p></li>
-<li><p>Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.</p></li>
-<li><p>Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.</p></li>
-<li><p>Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.</p></li>
-</ul>
-<p>We will use this to document the model development process as we go along and generate the model report at the end.</p>
-<p><code class="docutils literal notranslate"><span class="pre">The</span> <span class="pre">model</span> <span class="pre">report</span> <span class="pre">tool</span> <span class="pre">is</span> <span class="pre">a</span> <span class="pre">work</span> <span class="pre">in</span> <span class="pre">progress</span> <span class="pre">and</span> <span class="pre">is</span> <span class="pre">subject</span> <span class="pre">to</span> <span class="pre">change.</span></code></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">ModelCardReport</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Constants">
-<h2>Constants<a class="headerlink" href="#Constants" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">RANDOM_SEED</span> <span class="o">=</span> <span class="mi">85</span>
-<span class="n">NAN_THRESHOLD</span> <span class="o">=</span> <span class="mf">0.75</span>
-<span class="n">TRAIN_SIZE</span> <span class="o">=</span> <span class="mf">0.8</span>
-<span class="n">EVAL_NUM</span> <span class="o">=</span> <span class="mi">3</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Data-Loading">
-<h2>Data Loading<a class="headerlink" href="#Data-Loading" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">diabetes_130_data</span> <span class="o">=</span> <span class="n">fetch_ucirepo</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="mi">296</span><span class="p">)</span>
-<span class="n">features</span> <span class="o">=</span> <span class="n">diabetes_130_data</span><span class="p">[</span><span class="s2">&quot;data&quot;</span><span class="p">][</span><span class="s2">&quot;features&quot;</span><span class="p">]</span>
-<span class="n">targets</span> <span class="o">=</span> <span class="n">diabetes_130_data</span><span class="p">[</span><span class="s2">&quot;data&quot;</span><span class="p">][</span><span class="s2">&quot;targets&quot;</span><span class="p">]</span>
-<span class="n">metadata</span> <span class="o">=</span> <span class="n">diabetes_130_data</span><span class="p">[</span><span class="s2">&quot;metadata&quot;</span><span class="p">]</span>
-<span class="n">variables</span> <span class="o">=</span> <span class="n">diabetes_130_data</span><span class="p">[</span><span class="s2">&quot;variables&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metadata</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">transform_label</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Transform string labels of readmission into 0/1 binary labels.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    value: str</span>
-<span class="sd">        Input value</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    int</span>
-<span class="sd">        0 if not readmitted or if greater than 30 days, 1 if less than 30 days</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">value</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;NO&quot;</span><span class="p">,</span> <span class="s2">&quot;&gt;30&quot;</span><span class="p">]:</span>
-        <span class="k">return</span> <span class="mi">0</span>
-    <span class="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;&lt;30&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="mi">1</span>
-
-    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unexpected value for readmission!&quot;</span><span class="p">)</span>
-
-
-<span class="n">df</span> <span class="o">=</span> <span class="n">features</span>
-<span class="n">targets</span><span class="p">[</span><span class="s2">&quot;readmitted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">targets</span><span class="p">[</span><span class="s2">&quot;readmitted&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">transform_label</span><span class="p">)</span>
-<span class="n">df</span><span class="p">[</span><span class="s2">&quot;readmitted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">targets</span>
-</pre></div>
-</div>
-</div>
-<p>Choose a small subset for modelling</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1000</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<p>Remove features that are NaNs or have just a single unique value</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">features_to_remove</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df</span><span class="p">:</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">features_to_remove</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">features_to_remove</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<section id="Sex-values">
-<h3>Sex values<a class="headerlink" href="#Sex-values" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="s2">&quot;gender&quot;</span><span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Gender Distribution&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Gender Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Age-distribution">
-<h3>Age distribution<a class="headerlink" href="#Age-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">,</span>
-    <span class="n">bargap</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Outcome-distribution">
-<h3>Outcome distribution<a class="headerlink" href="#Outcome-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;readmitted&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;int&quot;</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;readmitted&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span><span class="n">textinfo</span><span class="o">=</span><span class="s2">&quot;percent+label&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span><span class="n">title_text</span><span class="o">=</span><span class="s2">&quot;Outcome Distribution&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span>
-    <span class="n">hovertemplate</span><span class="o">=</span><span class="s2">&quot;Outcome: %</span><span class="si">{label}</span><span class="s2">&lt;br&gt;Count: </span><span class="se">\</span>
-<span class="s2">    %</span><span class="si">{value}</span><span class="s2">&lt;br&gt;Percent: %</span><span class="si">{percent}</span><span class="s2">&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Outcome Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">class_counts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
-<span class="n">class_ratio</span> <span class="o">=</span> <span class="n">class_counts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">class_counts</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">class_ratio</span><span class="p">,</span> <span class="n">class_counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>From the features in the dataset, we select all of them to train the model!</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">features_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
-<span class="n">features_list</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s2">&quot;outcome&quot;</span><span class="p">)</span>
-<span class="n">features_list</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">features_list</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Identifying-feature-types">
-<h3>Identifying feature types<a class="headerlink" href="#Identifying-feature-types" title="Link to this heading">#</a></h3>
-<p>Cyclops <code class="docutils literal notranslate"><span class="pre">TabularFeatures</span></code> class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tab_features</span> <span class="o">=</span> <span class="n">TabularFeatures</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(),</span>
-    <span class="n">features</span><span class="o">=</span><span class="n">features_list</span><span class="p">,</span>
-    <span class="n">by</span><span class="o">=</span><span class="s2">&quot;index&quot;</span><span class="p">,</span>
-    <span class="n">targets</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">tab_features</span><span class="o">.</span><span class="n">types</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Creating-data-preprocessors">
-<h3>Creating data preprocessors<a class="headerlink" href="#Creating-data-preprocessors" title="Link to this heading">#</a></h3>
-<p>We create a data preprocessor using sklearn’s ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numeric_transformer</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span>
-    <span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;imputer&quot;</span><span class="p">,</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)),</span> <span class="p">(</span><span class="s2">&quot;scaler&quot;</span><span class="p">,</span> <span class="n">MinMaxScaler</span><span class="p">())],</span>
-<span class="p">)</span>
-
-<span class="n">binary_transformer</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span>
-    <span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;imputer&quot;</span><span class="p">,</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;most_frequent&quot;</span><span class="p">))],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numeric_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">((</span><span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;numeric&quot;</span><span class="p">)))</span>
-<span class="n">numeric_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">df</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">numeric_features</span>
-<span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">numeric_features</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">binary_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;binary&quot;</span><span class="p">))</span>
-<span class="n">binary_features</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s2">&quot;outcome&quot;</span><span class="p">)</span>
-<span class="n">ordinal_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span>
-    <span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;ordinal&quot;</span><span class="p">)</span>
-    <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;medical_specialty&quot;</span><span class="p">,</span> <span class="s2">&quot;diag_1&quot;</span><span class="p">,</span> <span class="s2">&quot;diag_2&quot;</span><span class="p">,</span> <span class="s2">&quot;diag_3&quot;</span><span class="p">]</span>
-<span class="p">)</span>
-<span class="n">binary_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">df</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">binary_features</span>
-<span class="p">]</span>
-<span class="n">ordinal_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">df</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">ordinal_features</span>
-<span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">binary_features</span><span class="p">,</span> <span class="n">ordinal_features</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">preprocessor</span> <span class="o">=</span> <span class="n">ColumnTransformer</span><span class="p">(</span>
-    <span class="n">transformers</span><span class="o">=</span><span class="p">[</span>
-        <span class="p">(</span><span class="s2">&quot;num&quot;</span><span class="p">,</span> <span class="n">numeric_transformer</span><span class="p">,</span> <span class="n">numeric_indices</span><span class="p">),</span>
-        <span class="p">(</span>
-            <span class="s2">&quot;onehot&quot;</span><span class="p">,</span>
-            <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">handle_unknown</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">sparse_output</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-            <span class="n">binary_indices</span> <span class="o">+</span> <span class="n">ordinal_indices</span><span class="p">,</span>
-        <span class="p">),</span>
-    <span class="p">],</span>
-    <span class="n">remainder</span><span class="o">=</span><span class="s2">&quot;passthrough&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Let’s document the dataset in the model card. This can be done using the <code class="docutils literal notranslate"><span class="pre">log_dataset</span></code> method, which takes the following arguments: - description: A description of the dataset. - citation: The citation for the dataset. - link: A link to a resource for the dataset. - license_id: The SPDX license identifier for the dataset. - version: The version of the dataset. - features: A list of features in the dataset. - split: The split of the dataset (train, test, validation, etc.). - sensitive_features:
-A list of sensitive features used to train/evaluate the model. - sensitive_feature_justification: A justification for the sensitive features used to train/evaluate the model.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_dataset</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;abstract&quot;</span><span class="p">],</span>
-    <span class="n">citation</span><span class="o">=</span><span class="n">inspect</span><span class="o">.</span><span class="n">cleandoc</span><span class="p">(</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        @article{strack2014impact,</span>
-<span class="sd">          title={Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records},</span>
-<span class="sd">          author={Strack, Beata and DeShazo, Jonathan P and Gennings, Chris and Olmo, Juan L and Ventura, Sebastian and Cios, Krzysztof J and Clore, John N and others},</span>
-<span class="sd">          journal={BioMed research international},</span>
-<span class="sd">          volume={2014},</span>
-<span class="sd">          year={2014},</span>
-<span class="sd">          publisher={Hindawi}</span>
-<span class="sd">        }</span>
-<span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
-    <span class="p">),</span>
-    <span class="n">link</span><span class="o">=</span><span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;repository_url&quot;</span><span class="p">],</span>
-    <span class="n">license_id</span><span class="o">=</span><span class="s2">&quot;CC0-1.0&quot;</span><span class="p">,</span>
-    <span class="n">version</span><span class="o">=</span><span class="s2">&quot;Version 1&quot;</span><span class="p">,</span>
-    <span class="n">features</span><span class="o">=</span><span class="n">features_list</span><span class="p">,</span>
-    <span class="n">sensitive_features</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;gender&quot;</span><span class="p">,</span> <span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="s2">&quot;race&quot;</span><span class="p">],</span>
-    <span class="n">sensitive_feature_justification</span><span class="o">=</span><span class="s2">&quot;Demographic information like age and gender </span><span class="se">\</span>
-<span class="s2">        often have a strong correlation with health outcomes. For example, older </span><span class="se">\</span>
-<span class="s2">        patients are more likely to have a higher risk of readmission.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="Creating-Hugging-Face-Dataset">
-<h2>Creating Hugging Face Dataset<a class="headerlink" href="#Creating-Hugging-Face-Dataset" title="Link to this heading">#</a></h2>
-<p>We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-<span class="n">dataset</span><span class="o">.</span><span class="n">cleanup_cache_files</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">cast_column</span><span class="p">(</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span> <span class="n">ClassLabel</span><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
-    <span class="n">train_size</span><span class="o">=</span><span class="n">TRAIN_SIZE</span><span class="p">,</span>
-    <span class="n">stratify_by_column</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span>
-    <span class="n">seed</span><span class="o">=</span><span class="n">RANDOM_SEED</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Model-Creation">
-<h2>Model Creation<a class="headerlink" href="#Model-Creation" title="Link to this heading">#</a></h2>
-<p>CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a SGD classifier to fit a logisitic regression model. The model configurations can be passed to <code class="docutils literal notranslate"><span class="pre">create_model</span></code> based on the sklearn parameters for SGDClassifier.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="s2">&quot;xgb_classifier&quot;</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">create_model</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Task-Creation">
-<h2>Task Creation<a class="headerlink" href="#Task-Creation" title="Link to this heading">#</a></h2>
-<p>We use Cyclops tasks to define our model’s task (in this case, readmission prediction), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">readmission_prediction_task</span> <span class="o">=</span> <span class="n">BinaryTabularClassificationTask</span><span class="p">(</span>
-    <span class="p">{</span><span class="n">model_name</span><span class="p">:</span> <span class="n">model</span><span class="p">},</span>
-    <span class="n">task_features</span><span class="o">=</span><span class="n">features_list</span><span class="p">,</span>
-    <span class="n">task_target</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">readmission_prediction_task</span><span class="o">.</span><span class="n">list_models</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Training">
-<h2>Training<a class="headerlink" href="#Training" title="Link to this heading">#</a></h2>
-<p>If <code class="docutils literal notranslate"><span class="pre">best_model_params</span></code> is passed to the <code class="docutils literal notranslate"><span class="pre">train</span></code> method, the best model will be selected after the hyperparameter search. The parameters in <code class="docutils literal notranslate"><span class="pre">best_model_params</span></code> indicate the values to create the parameters grid.</p>
-<p>Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">best_model_params</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;n_estimators&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">500</span><span class="p">],</span>
-    <span class="s2">&quot;learning_rate&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">],</span>
-    <span class="s2">&quot;max_depth&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
-    <span class="s2">&quot;reg_lambda&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span>
-    <span class="s2">&quot;colsample_bytree&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
-    <span class="s2">&quot;gamma&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span>
-    <span class="s2">&quot;method&quot;</span><span class="p">:</span> <span class="s2">&quot;random&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;scale_pos_weight&quot;</span><span class="p">:</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">class_ratio</span><span class="p">)],</span>
-<span class="p">}</span>
-<span class="n">readmission_prediction_task</span><span class="o">.</span><span class="n">train</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">best_model_params</span><span class="o">=</span><span class="n">best_model_params</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_params</span> <span class="o">=</span> <span class="n">readmission_prediction_task</span><span class="o">.</span><span class="n">list_models_params</span><span class="p">()[</span><span class="n">model_name</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">model_params</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Log the model parameters to the report.</strong></p>
-<p>We can add model parameters to the model card using the <code class="docutils literal notranslate"><span class="pre">log_model_parameters</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_model_parameters</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="n">model_params</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Prediction">
-<h2>Prediction<a class="headerlink" href="#Prediction" title="Link to this heading">#</a></h2>
-<p>The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">readmission_prediction_task</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">proba</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">only_predictions</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">prediction_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;y_prob&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">y_pred_i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">y_pred_i</span> <span class="ow">in</span> <span class="n">y_pred</span><span class="p">],</span>
-        <span class="s2">&quot;y_true&quot;</span><span class="p">:</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">][</span><span class="s2">&quot;outcome&quot;</span><span class="p">],</span>
-        <span class="s2">&quot;gender&quot;</span><span class="p">:</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">][</span><span class="s2">&quot;gender&quot;</span><span class="p">],</span>
-    <span class="p">}</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Evaluation">
-<h2>Evaluation<a class="headerlink" href="#Evaluation" title="Link to this heading">#</a></h2>
-<p>Evaluation is done using various evaluation metrics that provide different perspectives on the model’s predictive abilities i.e. standard performance metrics and fairness metrics.</p>
-<p>The standard performance metrics can be created using the <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code> object.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metric_names</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;binary_accuracy&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_precision&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_recall&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_f1_score&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_auroc&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_average_precision&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_roc_curve&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_precision_recall_curve&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric_name</span> <span class="ow">in</span> <span class="n">metric_names</span>
-<span class="p">]</span>
-<span class="n">metric_collection</span> <span class="o">=</span> <span class="n">MetricDict</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using <code class="docutils literal notranslate"><span class="pre">SliceSpec</span></code> objects.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">spec_list</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;[50-60)&quot;</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;[60-70)&quot;</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-<span class="p">]</span>
-<span class="n">slice_spec</span> <span class="o">=</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>A <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code> can also be defined for the fairness metrics.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">specificity</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;binary_specificity&quot;</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">sensitivity</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;binary_sensitivity&quot;</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="n">fpr</span> <span class="o">=</span> <span class="o">-</span><span class="n">specificity</span> <span class="o">+</span> <span class="mi">1</span>
-<span class="n">fnr</span> <span class="o">=</span> <span class="o">-</span><span class="n">sensitivity</span> <span class="o">+</span> <span class="mi">1</span>
-
-<span class="n">ber</span> <span class="o">=</span> <span class="p">(</span><span class="n">fpr</span> <span class="o">+</span> <span class="n">fnr</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
-
-<span class="n">fairness_metric_collection</span> <span class="o">=</span> <span class="n">MetricDict</span><span class="p">(</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Sensitivity&quot;</span><span class="p">:</span> <span class="n">sensitivity</span><span class="p">,</span>
-        <span class="s2">&quot;Specificity&quot;</span><span class="p">:</span> <span class="n">specificity</span><span class="p">,</span>
-        <span class="s2">&quot;BER&quot;</span><span class="p">:</span> <span class="n">ber</span><span class="p">,</span>
-    <span class="p">},</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The FairnessConfig helps in setting up and evaluating the fairness of the model predictions.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fairness_config</span> <span class="o">=</span> <span class="n">FairnessConfig</span><span class="p">(</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="n">fairness_metric_collection</span><span class="p">,</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># dataset is passed from the evaluator</span>
-    <span class="n">target_columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># target columns are passed from the evaluator</span>
-    <span class="n">groups</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">],</span>
-    <span class="n">group_base_values</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="s2">&quot;[40-50)&quot;</span><span class="p">},</span>
-    <span class="n">thresholds</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="p">,</span> <span class="n">dataset_with_preds</span> <span class="o">=</span> <span class="n">readmission_prediction_task</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="n">metric_collection</span><span class="p">,</span>
-    <span class="n">model_names</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">prediction_column_prefix</span><span class="o">=</span><span class="s2">&quot;preds&quot;</span><span class="p">,</span>
-    <span class="n">slice_spec</span><span class="o">=</span><span class="n">slice_spec</span><span class="p">,</span>
-    <span class="n">batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">fairness_config</span><span class="o">=</span><span class="n">fairness_config</span><span class="p">,</span>
-    <span class="n">override_fairness_metrics</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results_female</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">readmission_prediction_task</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="n">MetricDict</span><span class="p">(</span>
-        <span class="p">{</span>
-            <span class="s2">&quot;BinaryAccuracy&quot;</span><span class="p">:</span> <span class="n">create_metric</span><span class="p">(</span>
-                <span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;binary_accuracy&quot;</span><span class="p">,</span>
-                <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="p">),</span>
-        <span class="p">},</span>
-    <span class="p">),</span>
-    <span class="n">model_names</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">prediction_column_prefix</span><span class="o">=</span><span class="s2">&quot;preds&quot;</span><span class="p">,</span>
-    <span class="n">slice_spec</span><span class="o">=</span><span class="n">SliceSpec</span><span class="p">([{</span><span class="s2">&quot;gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;Female&quot;</span><span class="p">}}],</span> <span class="n">include_overall</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-    <span class="n">batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Log the performance metrics to the report.</strong></p>
-<p>We can add a performance metric to the model card using the <code class="docutils literal notranslate"><span class="pre">log_performance_metric</span></code> method, which expects a dictionary where the keys are in the following format: <code class="docutils literal notranslate"><span class="pre">slice_name/metric_name</span></code>. For instance, <code class="docutils literal notranslate"><span class="pre">overall/accuracy</span></code>.</p>
-<p>We first need to process the evaluation results to get the metrics in the right format.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;model_for_preds.</span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="n">results_flat</span> <span class="o">=</span> <span class="n">flatten_results_dict</span><span class="p">(</span>
-    <span class="n">results</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
-    <span class="n">remove_metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">results_female_flat</span> <span class="o">=</span> <span class="n">flatten_results_dict</span><span class="p">(</span>
-    <span class="n">results</span><span class="o">=</span><span class="n">results_female</span><span class="p">,</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="c1"># ruff: noqa: W505</span>
-<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">results_female_flat</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">split</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)</span>  <span class="c1"># noqa: PLW2901</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;BinaryPrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of predicted positive instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryRecall&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAccuracy&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of all instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAveragePrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryF1Score&quot;</span><span class="p">:</span> <span class="s2">&quot;The harmonic mean of precision and recall.&quot;</span><span class="p">,</span>
-    <span class="p">}</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">log_quantitative_analysis</span><span class="p">(</span>
-        <span class="s2">&quot;performance&quot;</span><span class="p">,</span>
-        <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
-        <span class="n">value</span><span class="o">=</span><span class="n">metric</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-        <span class="n">description</span><span class="o">=</span><span class="n">descriptions</span><span class="p">[</span><span class="n">name</span><span class="p">],</span>
-        <span class="n">metric_slice</span><span class="o">=</span><span class="n">split</span><span class="p">,</span>
-        <span class="n">pass_fail_thresholds</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
-        <span class="n">pass_fail_threshold_fns</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">),</span>
-    <span class="p">)</span>
-
-<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">results_flat</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">split</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)</span>  <span class="c1"># noqa: PLW2901</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;BinaryPrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of predicted positive instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryRecall&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAccuracy&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of all instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAveragePrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryF1Score&quot;</span><span class="p">:</span> <span class="s2">&quot;The harmonic mean of precision and recall.&quot;</span><span class="p">,</span>
-    <span class="p">}</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">log_quantitative_analysis</span><span class="p">(</span>
-        <span class="s2">&quot;performance&quot;</span><span class="p">,</span>
-        <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
-        <span class="n">value</span><span class="o">=</span><span class="n">metric</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-        <span class="n">description</span><span class="o">=</span><span class="n">descriptions</span><span class="p">[</span><span class="n">name</span><span class="p">],</span>
-        <span class="n">metric_slice</span><span class="o">=</span><span class="n">split</span><span class="p">,</span>
-        <span class="n">pass_fail_thresholds</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
-        <span class="n">pass_fail_threshold_fns</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">),</span>
-    <span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>We can also use the <code class="docutils literal notranslate"><span class="pre">ClassificationPlotter</span></code> to plot the performance metrics and the add the figure to the model card using the <code class="docutils literal notranslate"><span class="pre">log_plotly_figure</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plotter</span> <span class="o">=</span> <span class="n">ClassificationPlotter</span><span class="p">(</span><span class="n">task_type</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">])</span>
-<span class="n">plotter</span><span class="o">.</span><span class="n">set_template</span><span class="p">(</span><span class="s2">&quot;plotly_white&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># extracting the ROC curves and AUROC results for all the slices</span>
-<span class="n">roc_curves</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">aurocs</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">roc_curves</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># plotting the ROC curves for all the slices</span>
-<span class="n">roc_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">roc_curve_comparison</span><span class="p">(</span><span class="n">roc_curves</span><span class="p">,</span> <span class="n">aurocs</span><span class="o">=</span><span class="n">aurocs</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">roc_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;ROC Curve for Female Patients&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">roc_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># extracting the precision-recall curves and average precision results for all the slices</span>
-<span class="n">pr_curves</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">average_precisions</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryAveragePrecision&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">pr_curves</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># plotting the precision-recall curves for all the slices</span>
-<span class="n">pr_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">precision_recall_curve_comparison</span><span class="p">(</span>
-    <span class="n">pr_curves</span><span class="p">,</span>
-    <span class="n">auprcs</span><span class="o">=</span><span class="n">average_precisions</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">pr_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Precision-Recall Curve Comparison&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">pr_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the overall classification metric values.</span>
-<span class="n">overall_performance</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-    <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;overall&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-    <span class="k">if</span> <span class="n">metric_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the overall classification metric values.</span>
-<span class="n">overall_performance_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_value</span><span class="p">(</span>
-    <span class="n">overall_performance</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Overall Performance&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">overall_performance_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Overall Performance&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">overall_performance_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the metric values for all the slices.</span>
-<span class="n">slice_metrics</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="p">{</span>
-        <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-        <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">slice_results</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">metric_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-    <span class="p">}</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the metric values for all the slices.</span>
-<span class="n">slice_metrics_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_comparison_bar</span><span class="p">(</span><span class="n">slice_metrics</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">slice_metrics_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Slice Metric Comparison&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">slice_metrics_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the metric values for all the slices.</span>
-<span class="c1"># ROC curve components</span>
-<span class="n">pred_probs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dataset_with_preds</span><span class="p">[</span><span class="s2">&quot;preds.xgb_classifier&quot;</span><span class="p">])</span>
-<span class="n">true_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dataset_with_preds</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">])</span>
-<span class="n">roc_curve</span> <span class="o">=</span> <span class="n">binary_roc</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">pred_probs</span><span class="p">)</span>
-<span class="n">ppv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">roc_curve</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span>
-<span class="n">npv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">roc_curve</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span>
-
-<span class="c1"># Calculate PPV and NPV for each threshold</span>
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">threshold</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">roc_curve</span><span class="o">.</span><span class="n">thresholds</span><span class="p">):</span>
-    <span class="c1"># Calculate PPV and NPV</span>
-    <span class="n">ppv</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">binary_ppv</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">pred_probs</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">)</span>
-    <span class="n">npv</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">binary_npv</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">pred_probs</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">)</span>
-<span class="n">runway_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">threshperf</span><span class="p">(</span><span class="n">roc_curve</span><span class="p">,</span> <span class="n">ppv</span><span class="p">,</span> <span class="n">npv</span><span class="p">,</span> <span class="n">pred_probs</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">runway_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Threshold-Performance plot&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">runway_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p>We can also plot the calibration curve of the model on the test set</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">calibration_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">calibration</span><span class="p">(</span>
-    <span class="n">prediction_df</span><span class="p">,</span> <span class="n">y_true_col</span><span class="o">=</span><span class="s2">&quot;y_true&quot;</span><span class="p">,</span> <span class="n">y_prob_col</span><span class="o">=</span><span class="s2">&quot;y_prob&quot;</span><span class="p">,</span> <span class="n">group_col</span><span class="o">=</span><span class="s2">&quot;gender&quot;</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">calibration_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Calibration plot&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">calibration_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Reformatting the fairness metrics</span>
-<span class="n">fairness_results</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;fairness&quot;</span><span class="p">])</span>
-<span class="n">fairness_metrics</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="c1"># remove the group size from the fairness results and add it to the slice name</span>
-<span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">fairness_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">group_size</span> <span class="o">=</span> <span class="n">slice_results</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;Group Size&quot;</span><span class="p">)</span>
-    <span class="n">fairness_metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">slice_name</span><span class="si">}</span><span class="s2"> (Size=</span><span class="si">{</span><span class="n">group_size</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_results</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the fairness metrics</span>
-<span class="n">fairness_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_comparison_scatter</span><span class="p">(</span>
-    <span class="n">fairness_metrics</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Fairness Metrics&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fairness_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Fairness Metrics&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;fairness analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fairness_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Report-Generation">
-<h2>Report Generation<a class="headerlink" href="#Report-Generation" title="Link to this heading">#</a></h2>
-<p>Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.</p>
-<p>Let’s start with populating the model details section, which includes the following fields by default: - description: A high-level description of the model and its usage for a general audience. - version: The version of the model. - owners: The individuals or organizations that own the model. - license: The license under which the model is made available. - citation: The citation for the model. - references: Links to resources that are relevant to the model. - path: The path to where the model
-is stored. - regulatory_requirements: The regulatory requirements that are relevant to the model.</p>
-<p>We can add additional fields to the model details section by passing a dictionary to the <code class="docutils literal notranslate"><span class="pre">log_from_dict</span></code> method and specifying the section name as <code class="docutils literal notranslate"><span class="pre">model_details</span></code>. You can also use the <code class="docutils literal notranslate"><span class="pre">log_descriptor</span></code> method to add a new field object with a <code class="docutils literal notranslate"><span class="pre">description</span></code> attribute to any section of the model card.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;Readmission Prediction Model&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;The model was trained on the Diabetes 130-US Hospitals for Years 1999-2008 </span><span class="se">\</span>
-<span class="s2">        dataset to predict risk of readmission within 30 days of discharge.&quot;</span><span class="p">,</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;model_details&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">report</span><span class="o">.</span><span class="n">log_version</span><span class="p">(</span>
-    <span class="n">version_str</span><span class="o">=</span><span class="s2">&quot;0.0.1&quot;</span><span class="p">,</span>
-    <span class="n">date</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">date</span><span class="o">.</span><span class="n">today</span><span class="p">()),</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Initial Release&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_owner</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;CyclOps Team&quot;</span><span class="p">,</span>
-    <span class="n">contact</span><span class="o">=</span><span class="s2">&quot;vectorinstitute.github.io/cyclops/&quot;</span><span class="p">,</span>
-    <span class="n">email</span><span class="o">=</span><span class="s2">&quot;cyclops@vectorinstitute.ai&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_license</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">&quot;Apache-2.0&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_reference</span><span class="p">(</span>
-    <span class="n">link</span><span class="o">=</span><span class="s2">&quot;https://xgboost.readthedocs.io/en/stable/python/python_api.html&quot;</span><span class="p">,</span>  <span class="c1"># noqa: E501</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Next, let’s populate the considerations section, which includes the following fields by default: - users: The intended users of the model. - use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases. - fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms. - ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This
-can be populated using the <code class="docutils literal notranslate"><span class="pre">log_risk</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;users&quot;</span><span class="p">:</span> <span class="p">[</span>
-            <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;Hospitals&quot;</span><span class="p">},</span>
-            <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;Clinicians&quot;</span><span class="p">},</span>
-        <span class="p">],</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;considerations&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_user</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;ML Engineers&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_use_case</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Predicting risk of readmission.&quot;</span><span class="p">,</span>
-    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;primary&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_use_case</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Predicting risk of pathologies and conditions other</span><span class="se">\</span>
-<span class="s2">    than risk of readmission.&quot;</span><span class="p">,</span>
-    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;out-of-scope&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_fairness_assessment</span><span class="p">(</span>
-    <span class="n">affected_group</span><span class="o">=</span><span class="s2">&quot;sex, age&quot;</span><span class="p">,</span>
-    <span class="n">benefit</span><span class="o">=</span><span class="s2">&quot;Improved health outcomes for patients.&quot;</span><span class="p">,</span>
-    <span class="n">harm</span><span class="o">=</span><span class="s2">&quot;Biased predictions for patients in certain groups (e.g. older patients) </span><span class="se">\</span>
-<span class="s2">        may lead to worse health outcomes.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;We will monitor the performance of the model on these groups </span><span class="se">\</span>
-<span class="s2">        and retrain the model if the performance drops below a certain threshold.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_risk</span><span class="p">(</span>
-    <span class="n">risk</span><span class="o">=</span><span class="s2">&quot;The model may be used to make decisions that affect the health of patients.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;The model should be continuously monitored for performance </span><span class="se">\</span>
-<span class="s2">        and retrained if the performance drops below a certain threshold.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Once the model card is populated, you can generate the report using the <code class="docutils literal notranslate"><span class="pre">export</span></code> method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named <code class="docutils literal notranslate"><span class="pre">cyclops_reports</span></code> in the current working directory. You can change the path by passing a <code class="docutils literal notranslate"><span class="pre">output_dir</span></code> argument when instantiating the <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> class.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">synthetic_timestamps</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span>
-    <span class="n">start</span><span class="o">=</span><span class="s2">&quot;20/6/2024&quot;</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s2">&quot;W&quot;</span>
-<span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
-<span class="n">report_path</span> <span class="o">=</span> <span class="n">report</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
-    <span class="n">output_filename</span><span class="o">=</span><span class="s2">&quot;readmission_report_periodic.html&quot;</span><span class="p">,</span>
-    <span class="n">synthetic_timestamp</span><span class="o">=</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="n">EVAL_NUM</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span>
-    <span class="n">last_n_evals</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>You can view the generated HTML <a class="reference external" href="./heart_failure_report_periodic.html">report</a>.</p>
-</section>
-</section>
-
-        </article>
-      </div>
-      <footer>
-        
-        <div class="related-pages">
-          <a class="next-page" href="../nihcxr/cxr_classification.html">
-              <div class="page-info">
-                <div class="context">
-                  <span>Next</span>
-                </div>
-                <div class="title">Chest X-Ray Disease Classification</div>
-              </div>
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-            </a>
-          <a class="prev-page" href="../synthea/los_prediction.html">
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-              <div class="page-info">
-                <div class="context">
-                  <span>Previous</span>
-                </div>
-                
-                <div class="title">Prolonged Length of Stay Prediction</div>
-                
-              </div>
-            </a>
-        </div>
-        <div class="bottom-of-page">
-          <div class="left-details">
-            <div class="copyright">
-                Copyright &#169; 2024, Vector AI Engineering
-            </div>
-            <!--
-            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-            
-            <a href="https://github.com/pradyunsg/furo">Furo</a>
-            
-            -->
-          </div>
-          <div class="right-details">
-            <div class="icons">
-              <a class="muted-link " href="https://github.com/VectorInstitute/cyclops" aria-label="GitHub">
-                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
-                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
-                </svg>
-            </a>
-              
-            </div>
-          </div>
-        </div>
-        <div class="footer-container" style="text-align: center;">
-          <div class="footer-small-text">
-              <br> All <a href="https://github.com/VectorInstitute/cyclops">source code for cyclops</a> is freely available under the terms of an <a href="https://opensource.org/licenses/Apache-2.0">Apache-2.0</a> license.
-          </div>
-
-          <div class="footer-small-text">
-            This work is made possible due to the data obtained from the <a href="https://www.geminimedicine.ca/">GEMINI study</a>.
-          </div>
-
-          <div class="logo">
-            <a class="logo" href="https://vectorinstitute.ai/">
-              <span class="logo"></span>
-              <img alt="Logo of the Vector Institute" style="width:90px; max-width:90px;" src="../../_static/logos/vector_logo.png" />
-            </a>
-            <a class="logo" href="https://www.geminimedicine.ca/">
-              <span class="logo"></span>
-              <img alt="Logo of GEMINI" style="width:90px; max-width:90px;" src="../../_static/logos/gemini_logo.png" />
-            </a>
-          </div>
-        </div>
-        
-      </footer>
-    </div>
-    <aside class="toc-drawer">
-      
-      
-      <div class="toc-sticky toc-scroll">
-        <div class="toc-title-container">
-          <span class="toc-title">
-            On this page
-          </span>
-        </div>
-        <div class="toc-tree-container">
-          <div class="toc-tree">
-            <ul>
-<li><a class="reference internal" href="#">Readmission Prediction</a><ul>
-<li><a class="reference internal" href="#Install-libraries">Install libraries</a></li>
-<li><a class="reference internal" href="#Import-Libraries">Import Libraries</a></li>
-<li><a class="reference internal" href="#Constants">Constants</a></li>
-<li><a class="reference internal" href="#Data-Loading">Data Loading</a><ul>
-<li><a class="reference internal" href="#Sex-values">Sex values</a></li>
-<li><a class="reference internal" href="#Age-distribution">Age distribution</a></li>
-<li><a class="reference internal" href="#Outcome-distribution">Outcome distribution</a></li>
-<li><a class="reference internal" href="#Identifying-feature-types">Identifying feature types</a></li>
-<li><a class="reference internal" href="#Creating-data-preprocessors">Creating data preprocessors</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#Creating-Hugging-Face-Dataset">Creating Hugging Face Dataset</a></li>
-<li><a class="reference internal" href="#Model-Creation">Model Creation</a></li>
-<li><a class="reference internal" href="#Task-Creation">Task Creation</a></li>
-<li><a class="reference internal" href="#Training">Training</a></li>
-<li><a class="reference internal" href="#Prediction">Prediction</a></li>
-<li><a class="reference internal" href="#Evaluation">Evaluation</a></li>
-<li><a class="reference internal" href="#Report-Generation">Report Generation</a></li>
-</ul>
-</li>
-</ul>
-
-          </div>
-        </div>
-      </div>
-      
-      
-    </aside>
-  </div>
-</div></body>
-</html>
\ No newline at end of file
diff --git a/api/tutorials/diabetes_130/readmission_prediction.ipynb b/api/tutorials/diabetes_130/readmission_prediction.ipynb
deleted file mode 100644
index 4e50a97d1..000000000
--- a/api/tutorials/diabetes_130/readmission_prediction.ipynb
+++ /dev/null
@@ -1,1417 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Readmission Prediction\n",
-    "\n",
-    "This notebook showcases readmission prediction on the [Diabetes 130-US Hospitals for Years 1999-2008](https://archive.ics.uci.edu/dataset/296/diabetes+130-us+hospitals+for+years+1999-2008) using CyclOps. The task is formulated as a binary classification task, where we predict the probability of early readmission of the patient within 30 days of discharge."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Install libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install pycyclops\n",
-    "!pip install ucimlrepo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"Readmission prediction.\"\"\"\n",
-    "\n",
-    "# ruff: noqa: E402\n",
-    "\n",
-    "import copy\n",
-    "import inspect\n",
-    "from datetime import date\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "from datasets import Dataset\n",
-    "from datasets.features import ClassLabel\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
-    "from ucimlrepo import fetch_ucirepo\n",
-    "\n",
-    "from cyclops.data.df.feature import TabularFeatures\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate.fairness import FairnessConfig  # noqa: E402\n",
-    "from cyclops.evaluate.metrics import create_metric\n",
-    "from cyclops.evaluate.metrics.experimental.functional import (\n",
-    "    binary_npv,\n",
-    "    binary_ppv,\n",
-    "    binary_roc,\n",
-    ")\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.models.catalog import create_model\n",
-    "from cyclops.report import ModelCardReport\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter\n",
-    "from cyclops.report.utils import flatten_results_dict\n",
-    "from cyclops.tasks import BinaryTabularClassificationTask"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "CyclOps offers a package for documentation of the model through a model report. The `ModelCardReport` class is used to populate and generate the model report as an HTML file. The model report has the following sections:\n",
-    "\n",
-    "- Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).\n",
-    "- Datasets: High level statistics of the training data, including changes in distribution over time.\n",
-    "- Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.\n",
-    "- Fairness Analysis: This section contains the fairness metrics of the model.\n",
-    "- Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.\n",
-    "- Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.\n",
-    "- Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.\n",
-    "\n",
-    "We will use this to document the model development process as we go along and generate the model report at the end.\n",
-    "\n",
-    "`The model report tool is a work in progress and is subject to change.`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "RANDOM_SEED = 85\n",
-    "NAN_THRESHOLD = 0.75\n",
-    "TRAIN_SIZE = 0.8\n",
-    "EVAL_NUM = 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Loading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "diabetes_130_data = fetch_ucirepo(id=296)\n",
-    "features = diabetes_130_data[\"data\"][\"features\"]\n",
-    "targets = diabetes_130_data[\"data\"][\"targets\"]\n",
-    "metadata = diabetes_130_data[\"metadata\"]\n",
-    "variables = diabetes_130_data[\"variables\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "metadata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def transform_label(value):\n",
-    "    \"\"\"Transform string labels of readmission into 0/1 binary labels.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    value: str\n",
-    "        Input value\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    int\n",
-    "        0 if not readmitted or if greater than 30 days, 1 if less than 30 days\n",
-    "\n",
-    "    \"\"\"\n",
-    "    if value in [\"NO\", \">30\"]:\n",
-    "        return 0\n",
-    "    if value == \"<30\":\n",
-    "        return 1\n",
-    "\n",
-    "    raise ValueError(\"Unexpected value for readmission!\")\n",
-    "\n",
-    "\n",
-    "df = features\n",
-    "targets[\"readmitted\"] = targets[\"readmitted\"].apply(transform_label)\n",
-    "df[\"readmitted\"] = targets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Choose a small subset for modelling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df[0:1000]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Remove features that are NaNs or have just a single unique value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "features_to_remove = []\n",
-    "for col in df:\n",
-    "    if len(df[col].value_counts()) <= 1:\n",
-    "        features_to_remove.append(col)\n",
-    "df = df.drop(columns=features_to_remove)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sex values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"gender\")\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    title=\"Gender Distribution\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Gender Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###  Age distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(df, x=\"age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Age Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Outcome distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "df[\"outcome\"] = df[\"readmitted\"].astype(\"int\")\n",
-    "df = df.drop(columns=[\"readmitted\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"outcome\")\n",
-    "fig.update_traces(textinfo=\"percent+label\")\n",
-    "fig.update_layout(title_text=\"Outcome Distribution\")\n",
-    "fig.update_traces(\n",
-    "    hovertemplate=\"Outcome: %{label}<br>Count: \\\n",
-    "    %{value}<br>Percent: %{percent}\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Outcome Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "class_counts = df[\"outcome\"].value_counts()\n",
-    "class_ratio = class_counts[0] / class_counts[1]\n",
-    "print(class_ratio, class_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From the features in the dataset, we select all of them to train the model!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "features_list = list(df.columns)\n",
-    "features_list.remove(\"outcome\")\n",
-    "features_list = sorted(features_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Identifying feature types\n",
-    "\n",
-    "Cyclops `TabularFeatures` class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "tab_features = TabularFeatures(\n",
-    "    data=df.reset_index(),\n",
-    "    features=features_list,\n",
-    "    by=\"index\",\n",
-    "    targets=\"outcome\",\n",
-    ")\n",
-    "print(tab_features.types)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating data preprocessors\n",
-    "\n",
-    "We create a data preprocessor using sklearn's ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"mean\")), (\"scaler\", MinMaxScaler())],\n",
-    ")\n",
-    "\n",
-    "binary_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"most_frequent\"))],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_features = sorted((tab_features.features_by_type(\"numeric\")))\n",
-    "numeric_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in numeric_features\n",
-    "]\n",
-    "print(numeric_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "binary_features = sorted(tab_features.features_by_type(\"binary\"))\n",
-    "binary_features.remove(\"outcome\")\n",
-    "ordinal_features = sorted(\n",
-    "    tab_features.features_by_type(\"ordinal\")\n",
-    "    + [\"medical_specialty\", \"diag_1\", \"diag_2\", \"diag_3\"]\n",
-    ")\n",
-    "binary_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in binary_features\n",
-    "]\n",
-    "ordinal_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in ordinal_features\n",
-    "]\n",
-    "print(binary_features, ordinal_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "preprocessor = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        (\"num\", numeric_transformer, numeric_indices),\n",
-    "        (\n",
-    "            \"onehot\",\n",
-    "            OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False),\n",
-    "            binary_indices + ordinal_indices,\n",
-    "        ),\n",
-    "    ],\n",
-    "    remainder=\"passthrough\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's document the dataset in the model card. This can be done using the `log_dataset` method, which takes the following arguments:\n",
-    "- description: A description of the dataset.\n",
-    "- citation: The citation for the dataset.\n",
-    "- link: A link to a resource for the dataset.\n",
-    "- license_id: The SPDX license identifier for the dataset.\n",
-    "- version: The version of the dataset.\n",
-    "- features: A list of features in the dataset.\n",
-    "- split: The split of the dataset (train, test, validation, etc.).\n",
-    "- sensitive_features: A list of sensitive features used to train/evaluate the model.\n",
-    "- sensitive_feature_justification: A justification for the sensitive features used to train/evaluate the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_dataset(\n",
-    "    description=metadata[\"abstract\"],\n",
-    "    citation=inspect.cleandoc(\n",
-    "        \"\"\"\n",
-    "        @article{strack2014impact,\n",
-    "          title={Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records},\n",
-    "          author={Strack, Beata and DeShazo, Jonathan P and Gennings, Chris and Olmo, Juan L and Ventura, Sebastian and Cios, Krzysztof J and Clore, John N and others},\n",
-    "          journal={BioMed research international},\n",
-    "          volume={2014},\n",
-    "          year={2014},\n",
-    "          publisher={Hindawi}\n",
-    "        }\n",
-    "    \"\"\",\n",
-    "    ),\n",
-    "    link=metadata[\"repository_url\"],\n",
-    "    license_id=\"CC0-1.0\",\n",
-    "    version=\"Version 1\",\n",
-    "    features=features_list,\n",
-    "    sensitive_features=[\"gender\", \"age\", \"race\"],\n",
-    "    sensitive_feature_justification=\"Demographic information like age and gender \\\n",
-    "        often have a strong correlation with health outcomes. For example, older \\\n",
-    "        patients are more likely to have a higher risk of readmission.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating Hugging Face Dataset\n",
-    "\n",
-    "We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = Dataset.from_pandas(df)\n",
-    "dataset.cleanup_cache_files()\n",
-    "print(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = dataset.cast_column(\"outcome\", ClassLabel(num_classes=2))\n",
-    "dataset = dataset.train_test_split(\n",
-    "    train_size=TRAIN_SIZE,\n",
-    "    stratify_by_column=\"outcome\",\n",
-    "    seed=RANDOM_SEED,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model Creation\n",
-    "\n",
-    "CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a SGD classifier to fit a logisitic regression model. The model configurations can be passed to `create_model` based on the sklearn parameters for SGDClassifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_name = \"xgb_classifier\"\n",
-    "model = create_model(model_name, random_state=123)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Task Creation\n",
-    "\n",
-    "We use Cyclops tasks to define our model's task (in this case, readmission prediction), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "readmission_prediction_task = BinaryTabularClassificationTask(\n",
-    "    {model_name: model},\n",
-    "    task_features=features_list,\n",
-    "    task_target=\"outcome\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "readmission_prediction_task.list_models()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training\n",
-    "\n",
-    "If `best_model_params` is passed to the `train` method, the best model will be selected after the hyperparameter search. The parameters in `best_model_params` indicate the values to create the parameters grid.\n",
-    "\n",
-    "Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "best_model_params = {\n",
-    "    \"n_estimators\": [100, 250, 500],\n",
-    "    \"learning_rate\": [0.1, 0.01],\n",
-    "    \"max_depth\": [2, 5],\n",
-    "    \"reg_lambda\": [0, 1, 10],\n",
-    "    \"colsample_bytree\": [0.7, 0.8, 1],\n",
-    "    \"gamma\": [0, 1, 2, 10],\n",
-    "    \"method\": \"random\",\n",
-    "    \"scale_pos_weight\": [int(class_ratio)],\n",
-    "}\n",
-    "readmission_prediction_task.train(\n",
-    "    dataset[\"train\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    best_model_params=best_model_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_params = readmission_prediction_task.list_models_params()[model_name]\n",
-    "print(model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the model parameters to the report.**\n",
-    "\n",
-    "We can add model parameters to the model card using the `log_model_parameters` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_model_parameters(params=model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prediction\n",
-    "\n",
-    "The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "y_pred = readmission_prediction_task.predict(\n",
-    "    dataset[\"test\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    proba=True,\n",
-    "    only_predictions=True,\n",
-    ")\n",
-    "prediction_df = pd.DataFrame(\n",
-    "    {\n",
-    "        \"y_prob\": [y_pred_i[1] for y_pred_i in y_pred],\n",
-    "        \"y_true\": dataset[\"test\"][\"outcome\"],\n",
-    "        \"gender\": dataset[\"test\"][\"gender\"],\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluation\n",
-    "\n",
-    "Evaluation is done using various evaluation metrics that provide different perspectives on the model's predictive abilities i.e. standard performance metrics and fairness metrics.\n",
-    "\n",
-    "The standard performance metrics can be created using the `MetricDict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_auroc\",\n",
-    "    \"binary_average_precision\",\n",
-    "    \"binary_roc_curve\",\n",
-    "    \"binary_precision_recall_curve\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using `SliceSpec` objects. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"value\": \"[50-60)\",\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"value\": \"[60-70)\",\n",
-    "        },\n",
-    "    },\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `MetricDict` can also be defined for the fairness metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "specificity = create_metric(metric_name=\"binary_specificity\", experimental=True)\n",
-    "sensitivity = create_metric(metric_name=\"binary_sensitivity\", experimental=True)\n",
-    "\n",
-    "fpr = -specificity + 1\n",
-    "fnr = -sensitivity + 1\n",
-    "\n",
-    "ber = (fpr + fnr) / 2\n",
-    "\n",
-    "fairness_metric_collection = MetricDict(\n",
-    "    {\n",
-    "        \"Sensitivity\": sensitivity,\n",
-    "        \"Specificity\": specificity,\n",
-    "        \"BER\": ber,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The FairnessConfig helps in setting up and evaluating the fairness of the model predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fairness_config = FairnessConfig(\n",
-    "    metrics=fairness_metric_collection,\n",
-    "    dataset=None,  # dataset is passed from the evaluator\n",
-    "    target_columns=None,  # target columns are passed from the evaluator\n",
-    "    groups=[\"age\"],\n",
-    "    group_base_values={\"age\": \"[40-50)\"},\n",
-    "    thresholds=[0.5],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "results, dataset_with_preds = readmission_prediction_task.evaluate(\n",
-    "    dataset=dataset[\"test\"],\n",
-    "    metrics=metric_collection,\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=slice_spec,\n",
-    "    batch_size=-1,\n",
-    "    fairness_config=fairness_config,\n",
-    "    override_fairness_metrics=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_female, _ = readmission_prediction_task.evaluate(\n",
-    "    dataset=dataset[\"test\"],\n",
-    "    metrics=MetricDict(\n",
-    "        {\n",
-    "            \"BinaryAccuracy\": create_metric(\n",
-    "                metric_name=\"binary_accuracy\",\n",
-    "                experimental=True,\n",
-    "            ),\n",
-    "        },\n",
-    "    ),\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=SliceSpec([{\"gender\": {\"value\": \"Female\"}}], include_overall=False),\n",
-    "    batch_size=-1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the performance metrics to the report.**\n",
-    "\n",
-    "We can add a performance metric to the model card using the `log_performance_metric` method, which expects a dictionary where the keys are in the following format: `slice_name/metric_name`. For instance, `overall/accuracy`. \n",
-    "\n",
-    "We first need to process the evaluation results to get the metrics in the right format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_name = f\"model_for_preds.{model_name}\"\n",
-    "results_flat = flatten_results_dict(\n",
-    "    results=results,\n",
-    "    remove_metrics=[\"BinaryROC\", \"BinaryPrecisionRecallCurve\"],\n",
-    "    model_name=model_name,\n",
-    ")\n",
-    "results_female_flat = flatten_results_dict(\n",
-    "    results=results_female,\n",
-    "    model_name=model_name,\n",
-    ")\n",
-    "# ruff: noqa: W505\n",
-    "for name, metric in results_female_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )\n",
-    "\n",
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the `ClassificationPlotter` to plot the performance metrics and the add the figure to the model card using the `log_plotly_figure` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the ROC curves and AUROC results for all the slices\n",
-    "roc_curves = {\n",
-    "    slice_name: slice_results[\"BinaryROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "aurocs = {\n",
-    "    slice_name: slice_results[\"BinaryAUROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "roc_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the ROC curves for all the slices\n",
-    "roc_plot = plotter.roc_curve_comparison(roc_curves, aurocs=aurocs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=roc_plot,\n",
-    "    caption=\"ROC Curve for Female Patients\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "roc_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the precision-recall curves and average precision results for all the slices\n",
-    "pr_curves = {\n",
-    "    slice_name: slice_results[\"BinaryPrecisionRecallCurve\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "average_precisions = {\n",
-    "    slice_name: slice_results[\"BinaryAveragePrecision\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "pr_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the precision-recall curves for all the slices\n",
-    "pr_plot = plotter.precision_recall_curve_comparison(\n",
-    "    pr_curves,\n",
-    "    auprcs=average_precisions,\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=pr_plot,\n",
-    "    caption=\"Precision-Recall Curve Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "pr_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the overall classification metric values.\n",
-    "overall_performance = {\n",
-    "    metric_name: metric_value\n",
-    "    for metric_name, metric_value in results[model_name][\"overall\"].items()\n",
-    "    if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the overall classification metric values.\n",
-    "overall_performance_plot = plotter.metrics_value(\n",
-    "    overall_performance,\n",
-    "    title=\"Overall Performance\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=overall_performance_plot,\n",
-    "    caption=\"Overall Performance\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "overall_performance_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=slice_metrics_plot,\n",
-    "    caption=\"Slice Metric Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "# ROC curve components\n",
-    "pred_probs = np.array(dataset_with_preds[\"preds.xgb_classifier\"])\n",
-    "true_labels = np.array(dataset_with_preds[\"outcome\"])\n",
-    "roc_curve = binary_roc(true_labels, pred_probs)\n",
-    "ppv = np.zeros_like(roc_curve.thresholds)\n",
-    "npv = np.zeros_like(roc_curve.thresholds)\n",
-    "\n",
-    "# Calculate PPV and NPV for each threshold\n",
-    "for i, threshold in enumerate(roc_curve.thresholds):\n",
-    "    # Calculate PPV and NPV\n",
-    "    ppv[i] = binary_ppv(true_labels, pred_probs, threshold=threshold)\n",
-    "    npv[i] = binary_npv(true_labels, pred_probs, threshold=threshold)\n",
-    "runway_plot = plotter.threshperf(roc_curve, ppv, npv, pred_probs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=runway_plot,\n",
-    "    caption=\"Threshold-Performance plot\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "runway_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot the calibration curve of the model on the test set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "calibration_plot = plotter.calibration(\n",
-    "    prediction_df, y_true_col=\"y_true\", y_prob_col=\"y_prob\", group_col=\"gender\"\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=calibration_plot,\n",
-    "    caption=\"Calibration plot\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "calibration_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reformatting the fairness metrics\n",
-    "fairness_results = copy.deepcopy(results[\"fairness\"])\n",
-    "fairness_metrics = {}\n",
-    "# remove the group size from the fairness results and add it to the slice name\n",
-    "for slice_name, slice_results in fairness_results.items():\n",
-    "    group_size = slice_results.pop(\"Group Size\")\n",
-    "    fairness_metrics[f\"{slice_name} (Size={group_size})\"] = slice_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the fairness metrics\n",
-    "fairness_plot = plotter.metrics_comparison_scatter(\n",
-    "    fairness_metrics,\n",
-    "    title=\"Fairness Metrics\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fairness_plot,\n",
-    "    caption=\"Fairness Metrics\",\n",
-    "    section_name=\"fairness analysis\",\n",
-    ")\n",
-    "fairness_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Report Generation\n",
-    "\n",
-    "Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.\n",
-    "\n",
-    "\n",
-    "Let's start with populating the model details section, which includes the following fields by default:\n",
-    "- description: A high-level description of the model and its usage for a general audience.\n",
-    "- version: The version of the model.\n",
-    "- owners: The individuals or organizations that own the model.\n",
-    "- license: The license under which the model is made available.\n",
-    "- citation: The citation for the model.\n",
-    "- references: Links to resources that are relevant to the model.\n",
-    "- path: The path to where the model is stored.\n",
-    "- regulatory_requirements: The regulatory requirements that are relevant to the model.\n",
-    "\n",
-    "We can add additional fields to the model details section by passing a dictionary to the `log_from_dict` method and specifying the section name as `model_details`. You can also use the `log_descriptor` method to add a new field object with a `description` attribute to any section of the model card."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"Readmission Prediction Model\",\n",
-    "        \"description\": \"The model was trained on the Diabetes 130-US Hospitals for Years 1999-2008 \\\n",
-    "        dataset to predict risk of readmission within 30 days of discharge.\",\n",
-    "    },\n",
-    "    section_name=\"model_details\",\n",
-    ")\n",
-    "\n",
-    "report.log_version(\n",
-    "    version_str=\"0.0.1\",\n",
-    "    date=str(date.today()),\n",
-    "    description=\"Initial Release\",\n",
-    ")\n",
-    "report.log_owner(\n",
-    "    name=\"CyclOps Team\",\n",
-    "    contact=\"vectorinstitute.github.io/cyclops/\",\n",
-    "    email=\"cyclops@vectorinstitute.ai\",\n",
-    ")\n",
-    "report.log_license(identifier=\"Apache-2.0\")\n",
-    "report.log_reference(\n",
-    "    link=\"https://xgboost.readthedocs.io/en/stable/python/python_api.html\",  # noqa: E501\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, let's populate the considerations section, which includes the following fields by default:\n",
-    "- users: The intended users of the model.\n",
-    "- use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases.\n",
-    "- fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms.\n",
-    "- ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This can be populated using the  `log_risk` method.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"users\": [\n",
-    "            {\"description\": \"Hospitals\"},\n",
-    "            {\"description\": \"Clinicians\"},\n",
-    "        ],\n",
-    "    },\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_user(description=\"ML Engineers\")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of readmission.\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of pathologies and conditions other\\\n",
-    "    than risk of readmission.\",\n",
-    "    kind=\"out-of-scope\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"sex, age\",\n",
-    "    benefit=\"Improved health outcomes for patients.\",\n",
-    "    harm=\"Biased predictions for patients in certain groups (e.g. older patients) \\\n",
-    "        may lead to worse health outcomes.\",\n",
-    "    mitigation_strategy=\"We will monitor the performance of the model on these groups \\\n",
-    "        and retrain the model if the performance drops below a certain threshold.\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"The model may be used to make decisions that affect the health of patients.\",\n",
-    "    mitigation_strategy=\"The model should be continuously monitored for performance \\\n",
-    "        and retrained if the performance drops below a certain threshold.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the model card is populated, you can generate the report using the `export` method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named `cyclops_reports` in the current working directory. You can change the path by passing a `output_dir` argument when instantiating the `ModelCardReport` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "synthetic_timestamps = pd.date_range(\n",
-    "    start=\"20/6/2024\", periods=3, freq=\"W\"\n",
-    ").values.astype(str)\n",
-    "report_path = report.export(\n",
-    "    output_filename=\"readmission_report_periodic.html\",\n",
-    "    synthetic_timestamp=synthetic_timestamps[EVAL_NUM - 1],\n",
-    "    last_n_evals=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "You can view the generated HTML [report](./heart_failure_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/api/tutorials/kaggle/heart_failure_prediction.html b/api/tutorials/kaggle/heart_failure_prediction.html
deleted file mode 100644
index b26785adb..000000000
--- a/api/tutorials/kaggle/heart_failure_prediction.html
+++ /dev/null
@@ -1,1498 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en">
-  <head><meta charset="utf-8"/>
-    <meta name="viewport" content="width=device-width,initial-scale=1"/>
-    <meta name="color-scheme" content="light dark">
-    <meta name="google-site-verification" content="i0qQRaR9OA3tSz_9tDocdcXGY27Ox_cy4FrvTHD2C_0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-
-    
-      <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Mortality Prediction" href="../mimiciv/mortality_prediction.html" /><link rel="prev" title="Example use cases" href="../../tutorials_use_cases.html" />
-
-    <link rel="shortcut icon" href="../../_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
-
-    
-    <meta name="msvalidate.01" content="8D2A5032F006424CAF54C0DDCD16F666" />
-      
-        <title>Heart Failure Prediction - cyclops documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
-    <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="../../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
-    <link rel="stylesheet" type="text/css" href="../../_static/nbsphinx-code-cells.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/css/cyclops.css?v=9f20b50b" />
-    
-    
-
-
-<style>
-  body {
-    --color-code-background: #f8f8f8;
-  --color-code-foreground: black;
-  --color-card-background: #5e88f2;
-  
-  }
-  @media not print {
-    body[data-theme="dark"] {
-      --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-    }
-    @media (prefers-color-scheme: dark) {
-      body:not([data-theme="light"]) {
-        --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-      }
-    }
-  }
-</style><script src="../../_static/documentation_options.js?v=5929fcd5"></script>
-    <script src="../../_static/doctools.js?v=888ff710"></script>
-    <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="../../_static/scripts/furo.js?v=32e29ea5"></script>
-    <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
-    <script src="../../_static/copybutton.js?v=ccdb6887"></script>
-    <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-    <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script src="../../_static/require.min.js?v=53367eec"></script>
-    <script src="../../_static/custom.js?v=275847af"></script>
-    
-    <script src="https://kit.fontawesome.com/2c1f516901.js" crossorigin="anonymous"></script>
-
-  </head>
-  <body>
-    
-    <script>
-      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
-    </script>
-    
-
-<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
-  <symbol id="svg-toc" viewBox="0 0 24 24">
-    <title>Contents</title>
-    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
-      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-menu" viewBox="0 0 24 24">
-    <title>Menu</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
-      <line x1="3" y1="12" x2="21" y2="12"></line>
-      <line x1="3" y1="6" x2="21" y2="6"></line>
-      <line x1="3" y1="18" x2="21" y2="18"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
-    <title>Expand</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
-      <polyline points="9 18 15 12 9 6"></polyline>
-    </svg>
-  </symbol>
-  <symbol id="svg-sun" viewBox="0 0 24 24">
-    <title>Light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
-      <circle cx="12" cy="12" r="5"></circle>
-      <line x1="12" y1="1" x2="12" y2="3"></line>
-      <line x1="12" y1="21" x2="12" y2="23"></line>
-      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
-      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
-      <line x1="1" y1="12" x2="3" y2="12"></line>
-      <line x1="21" y1="12" x2="23" y2="12"></line>
-      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
-      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon" viewBox="0 0 24 24">
-    <title>Dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
-      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
-    </svg>
-  </symbol>
-  <symbol id="svg-sun-half" viewBox="0 0 24 24">
-    <title>Auto light/dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-      <circle cx="12" cy="12" r="9" />
-      <path d="M13 12h5" />
-      <path d="M13 15h4" />
-      <path d="M13 18h1" />
-      <path d="M13 9h4" />
-      <path d="M13 6h1" />
-    </svg>
-  </symbol>
-</svg>
-
-<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
-<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
-<label class="overlay sidebar-overlay" for="__navigation">
-  <div class="visually-hidden">Hide navigation sidebar</div>
-</label>
-<label class="overlay toc-overlay" for="__toc">
-  <div class="visually-hidden">Hide table of contents sidebar</div>
-</label>
-
-
-
-<div class="page">
-  <header class="mobile-header">
-    <div class="header-left">
-      <label class="nav-overlay-icon" for="__navigation">
-        <div class="visually-hidden">Toggle site navigation sidebar</div>
-        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
-      </label>
-    </div>
-    <div class="header-center">
-      <a href="../../index.html"><div class="brand">cyclops  documentation</div></a>
-    </div>
-    <div class="header-right">
-      <div class="theme-toggle-container theme-toggle-header">
-        <button class="theme-toggle">
-          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-        </button>
-      </div>
-      <label class="toc-overlay-icon toc-header-icon" for="__toc">
-        <div class="visually-hidden">Toggle table of contents sidebar</div>
-        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-      </label>
-    </div>
-  </header>
-  <aside class="sidebar-drawer">
-    <div class="sidebar-container">
-      
-      <div class="sidebar-sticky"><a class="sidebar-brand" href="../../index.html">
-  
-  <div class="sidebar-logo-container">
-    <img class="sidebar-logo" src="../../_static/cyclops_logo-dark.png" alt="Logo"/>
-  </div>
-  
-  
-</a><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
-  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
-  <input type="hidden" name="check_keywords" value="yes">
-  <input type="hidden" name="area" value="default">
-</form>
-<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <ul class="current">
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l2 current has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../developing.html">🧑🏿‍💻 Developing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing to cyclops</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../api.html">API Reference</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of API Reference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.data.html">cyclops.data</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of cyclops.data</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.html">cyclops.data.slicer</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of cyclops.data.slicer</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.compound_filter.html">cyclops.data.slicer.compound_filter</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html">cyclops.data.slicer.filter_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html">cyclops.data.slicer.filter_non_null</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_range.html">cyclops.data.slicer.filter_range</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html">cyclops.data.slicer.filter_string_contains</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_value.html">cyclops.data.slicer.filter_value</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.is_datetime.html">cyclops.data.slicer.is_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.overall.html">cyclops.data.slicer.overall</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html">cyclops.data.slicer.SliceSpec</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.html">cyclops.data.aggregate</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of cyclops.data.aggregate</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html">cyclops.data.aggregate.tabular_as_aggregated</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html">cyclops.data.aggregate.timestamp_ffill_agg</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html">cyclops.data.aggregate.Aggregator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.html">cyclops.data.features.medical_image</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of cyclops.data.features.medical_image</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html">cyclops.data.features.medical_image.MedicalImage</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.tasks.html">cyclops.tasks</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of cyclops.tasks</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.html">cyclops.tasks.classification</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of cyclops.tasks.classification</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html">cyclops.tasks.classification.BinaryTabularClassificationTask</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html">cyclops.tasks.classification.MultilabelImageClassificationTask</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/evaluator.html">Evaluator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/fairness_evaluator.html">Fairness Evaluator</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/metrics.html">Evaluation Metrics</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of Evaluation Metrics</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/accuracy.html">Accuracy</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/auroc.html">AUROC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/average_precision.html">Average Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f1score.html">F1-Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f_beta.html">F-beta Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision.html">Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision_recall_curve.html">PrecisionRecallCurve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/recall.html">Recall</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/roc.html">ROC Curve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/sensitivity.html">Sensitivity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/specificity.html">Specificity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/stat_scores.html">StatScores</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.monitor.html">cyclops.monitor</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of cyclops.monitor</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.html">cyclops.monitor.clinical_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of cyclops.monitor.clinical_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html">cyclops.monitor.clinical_applicator.ClinicalShiftApplicator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html">cyclops.monitor.synthetic_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of cyclops.monitor.synthetic_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html">cyclops.monitor.synthetic_applicator.binary_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html">cyclops.monitor.synthetic_applicator.feature_association_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html">cyclops.monitor.synthetic_applicator.feature_swap_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html">cyclops.monitor.synthetic_applicator.gaussian_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html">cyclops.monitor.synthetic_applicator.knockout_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html">cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.report.html">cyclops.report</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of cyclops.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.html">cyclops.report.report</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of cyclops.report.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.ModelCardReport.html">cyclops.report.report.ModelCardReport</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-</div>
-</div>
-
-      </div>
-      
-    </div>
-  </aside>
-  <div class="main">
-    <div class="content">
-      <div class="article-container">
-        <a href="#" class="back-to-top muted-link">
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
-            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
-          </svg>
-          <span>Back to top</span>
-        </a>
-        <div class="content-icon-container">
-          
-<div class="theme-toggle-container theme-toggle-content">
-            <button class="theme-toggle">
-              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-            </button>
-          </div>
-          <label class="toc-overlay-icon toc-content-icon" for="__toc">
-            <div class="visually-hidden">Toggle table of contents sidebar</div>
-            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-          </label>
-        </div>
-        <article role="main">
-          <section id="Heart-Failure-Prediction">
-<h1>Heart Failure Prediction<a class="headerlink" href="#Heart-Failure-Prediction" title="Link to this heading">#</a></h1>
-<p>This notebook showcases heart failure prediction on a <a class="reference external" href="https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction">popular Kaggle dataset</a> using CyclOps. The task is formulated as a binary classification task, where we predict the probability that a patient will have heart failure.</p>
-<section id="Import-Libraries">
-<h2>Import Libraries<a class="headerlink" href="#Import-Libraries" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;Heart failure prediction.&quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">inspect</span>
-<span class="kn">import</span> <span class="nn">shutil</span>
-<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">date</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
-<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">Dataset</span>
-<span class="kn">from</span> <span class="nn">datasets.features</span> <span class="kn">import</span> <span class="n">ClassLabel</span>
-<span class="kn">from</span> <span class="nn">kaggle.api.kaggle_api_extended</span> <span class="kn">import</span> <span class="n">KaggleApi</span>
-<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
-<span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
-<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">OneHotEncoder</span>
-
-<span class="kn">from</span> <span class="nn">cyclops.data.df.feature</span> <span class="kn">import</span> <span class="n">TabularFeatures</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.slicer</span> <span class="kn">import</span> <span class="n">SliceSpec</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.fairness</span> <span class="kn">import</span> <span class="n">FairnessConfig</span>  <span class="c1"># noqa: E402</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics</span> <span class="kn">import</span> <span class="n">create_metric</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics.experimental.functional</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">binary_npv</span><span class="p">,</span>
-    <span class="n">binary_ppv</span><span class="p">,</span>
-    <span class="n">binary_roc</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics.experimental.metric_dict</span> <span class="kn">import</span> <span class="n">MetricDict</span>
-<span class="kn">from</span> <span class="nn">cyclops.models.catalog</span> <span class="kn">import</span> <span class="n">create_model</span>
-<span class="kn">from</span> <span class="nn">cyclops.report</span> <span class="kn">import</span> <span class="n">ModelCardReport</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.plot.classification</span> <span class="kn">import</span> <span class="n">ClassificationPlotter</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.utils</span> <span class="kn">import</span> <span class="n">flatten_results_dict</span>
-<span class="kn">from</span> <span class="nn">cyclops.tasks</span> <span class="kn">import</span> <span class="n">BinaryTabularClassificationTask</span>
-<span class="kn">from</span> <span class="nn">cyclops.utils.file</span> <span class="kn">import</span> <span class="n">join</span><span class="p">,</span> <span class="n">load_dataframe</span>
-</pre></div>
-</div>
-</div>
-<p>CyclOps offers a package for documentation of the model through a model report. The <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> class is used to populate and generate the model report as an HTML file. The model report has the following sections:</p>
-<ul class="simple">
-<li><p>Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).</p></li>
-<li><p>Datasets: High level statistics of the training data, including changes in distribution over time.</p></li>
-<li><p>Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.</p></li>
-<li><p>Fairness Analysis: This section contains the fairness metrics of the model.</p></li>
-<li><p>Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.</p></li>
-<li><p>Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.</p></li>
-<li><p>Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.</p></li>
-</ul>
-<p>We will use this to document the model development process as we go along and generate the model report at the end.</p>
-<p><code class="docutils literal notranslate"><span class="pre">The</span> <span class="pre">model</span> <span class="pre">report</span> <span class="pre">tool</span> <span class="pre">is</span> <span class="pre">a</span> <span class="pre">work</span> <span class="pre">in</span> <span class="pre">progress</span> <span class="pre">and</span> <span class="pre">is</span> <span class="pre">subject</span> <span class="pre">to</span> <span class="pre">change.</span></code></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">ModelCardReport</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Constants">
-<h2>Constants<a class="headerlink" href="#Constants" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">DATA_DIR</span> <span class="o">=</span> <span class="s2">&quot;./data&quot;</span>
-<span class="n">RANDOM_SEED</span> <span class="o">=</span> <span class="mi">85</span>
-<span class="n">NAN_THRESHOLD</span> <span class="o">=</span> <span class="mf">0.75</span>
-<span class="n">TRAIN_SIZE</span> <span class="o">=</span> <span class="mf">0.8</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Data-Loading">
-<h2>Data Loading<a class="headerlink" href="#Data-Loading" title="Link to this heading">#</a></h2>
-<p>Before starting, make sure to install the Kaggle API by running <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">kaggle</span></code>. To use the Kaggle API, you need to sign up for a Kaggle account at <a class="reference external" href="https://www.kaggle.com">https://www.kaggle.com</a>. Then go to the ‘Account’ tab of your user profile (<code class="docutils literal notranslate"><span class="pre">https://www.kaggle.com/&lt;username&gt;/account</span></code>) and select ‘Create API Token’. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location <code class="docutils literal notranslate"><span class="pre">~/.kaggle/kaggle.json</span></code> on your machine.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">api</span> <span class="o">=</span> <span class="n">KaggleApi</span><span class="p">()</span>
-<span class="n">api</span><span class="o">.</span><span class="n">authenticate</span><span class="p">()</span>
-<span class="n">api</span><span class="o">.</span><span class="n">dataset_download_files</span><span class="p">(</span>
-    <span class="s2">&quot;fedesoriano/heart-failure-prediction&quot;</span><span class="p">,</span>
-    <span class="n">path</span><span class="o">=</span><span class="n">DATA_DIR</span><span class="p">,</span>
-    <span class="n">unzip</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">load_dataframe</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">DATA_DIR</span><span class="p">,</span> <span class="s2">&quot;heart.csv&quot;</span><span class="p">),</span> <span class="n">file_format</span><span class="o">=</span><span class="s2">&quot;csv&quot;</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
-<span class="n">df</span><span class="p">[</span><span class="s2">&quot;ID&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<section id="Sex-values">
-<h3>Sex values<a class="headerlink" href="#Sex-values" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="s2">&quot;Sex&quot;</span><span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Sex Distribution&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Sex Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Age-distribution">
-<h3>Age distribution<a class="headerlink" href="#Age-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">,</span>
-    <span class="n">bargap</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Outcome-distribution">
-<h3>Outcome distribution<a class="headerlink" href="#Outcome-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;HeartDisease&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;int&quot;</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;HeartDisease&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span><span class="n">textinfo</span><span class="o">=</span><span class="s2">&quot;percent+label&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span><span class="n">title_text</span><span class="o">=</span><span class="s2">&quot;Outcome Distribution&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span>
-    <span class="n">hovertemplate</span><span class="o">=</span><span class="s2">&quot;Outcome: %</span><span class="si">{label}</span><span class="s2">&lt;br&gt;Count: </span><span class="se">\</span>
-<span class="s2">    %</span><span class="si">{value}</span><span class="s2">&lt;br&gt;Percent: %</span><span class="si">{percent}</span><span class="s2">&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Outcome Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">class_counts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
-<span class="n">class_ratio</span> <span class="o">=</span> <span class="n">class_counts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">class_counts</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">class_ratio</span><span class="p">,</span> <span class="n">class_counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>From all the features in the dataset, we select 20 of them which was reported by <a class="reference external" href="https://pubmed.ncbi.nlm.nih.gov/34301649/">Li et al.</a> to be the most important features in this classification task.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">features_list</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;Age&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;Sex&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;ChestPainType&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;RestingBP&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;Cholesterol&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;FastingBS&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;RestingECG&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;MaxHR&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;ExerciseAngina&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;Oldpeak&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;ST_Slope&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-
-<span class="n">features_list</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">features_list</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Identifying-feature-types">
-<h3>Identifying feature types<a class="headerlink" href="#Identifying-feature-types" title="Link to this heading">#</a></h3>
-<p>Cyclops <code class="docutils literal notranslate"><span class="pre">TabularFeatures</span></code> class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tab_features</span> <span class="o">=</span> <span class="n">TabularFeatures</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(),</span>
-    <span class="n">features</span><span class="o">=</span><span class="n">features_list</span><span class="p">,</span>
-    <span class="n">by</span><span class="o">=</span><span class="s2">&quot;ID&quot;</span><span class="p">,</span>
-    <span class="n">targets</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">tab_features</span><span class="o">.</span><span class="n">types</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Creating-data-preprocessors">
-<h3>Creating data preprocessors<a class="headerlink" href="#Creating-data-preprocessors" title="Link to this heading">#</a></h3>
-<p>We create a data preprocessor using sklearn’s ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numeric_transformer</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span>
-    <span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;imputer&quot;</span><span class="p">,</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)),</span> <span class="p">(</span><span class="s2">&quot;scaler&quot;</span><span class="p">,</span> <span class="n">MinMaxScaler</span><span class="p">())],</span>
-<span class="p">)</span>
-
-<span class="n">binary_transformer</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span>
-    <span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;imputer&quot;</span><span class="p">,</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;most_frequent&quot;</span><span class="p">))],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numeric_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">((</span><span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;numeric&quot;</span><span class="p">)))</span>
-<span class="n">numeric_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">df</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">numeric_features</span>
-<span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">numeric_features</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">binary_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;binary&quot;</span><span class="p">))</span>
-<span class="n">binary_features</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s2">&quot;outcome&quot;</span><span class="p">)</span>
-<span class="n">ordinal_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;ordinal&quot;</span><span class="p">))</span>
-<span class="n">binary_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">df</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">binary_features</span>
-<span class="p">]</span>
-<span class="n">ordinal_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">df</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">ordinal_features</span>
-<span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">binary_features</span><span class="p">,</span> <span class="n">ordinal_features</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">preprocessor</span> <span class="o">=</span> <span class="n">ColumnTransformer</span><span class="p">(</span>
-    <span class="n">transformers</span><span class="o">=</span><span class="p">[</span>
-        <span class="p">(</span><span class="s2">&quot;num&quot;</span><span class="p">,</span> <span class="n">numeric_transformer</span><span class="p">,</span> <span class="n">numeric_indices</span><span class="p">),</span>
-        <span class="p">(</span>
-            <span class="s2">&quot;onehot&quot;</span><span class="p">,</span>
-            <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">handle_unknown</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">),</span>
-            <span class="n">binary_indices</span> <span class="o">+</span> <span class="n">ordinal_indices</span><span class="p">,</span>
-        <span class="p">),</span>
-    <span class="p">],</span>
-    <span class="n">remainder</span><span class="o">=</span><span class="s2">&quot;passthrough&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Let’s document the dataset in the model card. This can be done using the <code class="docutils literal notranslate"><span class="pre">log_dataset</span></code> method, which takes the following arguments: - description: A description of the dataset. - citation: The citation for the dataset. - link: A link to a resource for the dataset. - license_id: The SPDX license identifier for the dataset. - version: The version of the dataset. - features: A list of features in the dataset. - split: The split of the dataset (train, test, validation, etc.). - sensitive_features:
-A list of sensitive features used to train/evaluate the model. - sensitive_feature_justification: A justification for the sensitive features used to train/evaluate the model.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_dataset</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;This dataset was created by combining different datasets</span>
-<span class="s2">    already available independently but not combined before. In this dataset,</span>
-<span class="s2">    5 heart datasets are combined over 11 common features. Every dataset used</span>
-<span class="s2">    can be found under the Index of heart disease datasets from UCI</span>
-<span class="s2">    Machine Learning Repository on the following link:</span>
-<span class="s2">    https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/.&quot;&quot;&quot;</span><span class="p">,</span>
-    <span class="n">citation</span><span class="o">=</span><span class="n">inspect</span><span class="o">.</span><span class="n">cleandoc</span><span class="p">(</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        @misc{fedesoriano,</span>
-<span class="sd">          title={Heart Failure Prediction Dataset.},</span>
-<span class="sd">          author={Fedesoriano, F},</span>
-<span class="sd">          year={2021},</span>
-<span class="sd">          publisher={Kaggle}</span>
-<span class="sd">        }</span>
-<span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
-    <span class="p">),</span>
-    <span class="n">link</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">    https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction</span>
-<span class="s2">    &quot;&quot;&quot;</span><span class="p">,</span>
-    <span class="n">license_id</span><span class="o">=</span><span class="s2">&quot;CC0-1.0&quot;</span><span class="p">,</span>
-    <span class="n">version</span><span class="o">=</span><span class="s2">&quot;Version 1&quot;</span><span class="p">,</span>
-    <span class="n">features</span><span class="o">=</span><span class="n">features_list</span><span class="p">,</span>
-    <span class="n">sensitive_features</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Sex&quot;</span><span class="p">,</span> <span class="s2">&quot;Age&quot;</span><span class="p">],</span>
-    <span class="n">sensitive_feature_justification</span><span class="o">=</span><span class="s2">&quot;Demographic information like age and gender </span><span class="se">\</span>
-<span class="s2">        often have a strong correlation with health outcomes. For example, older </span><span class="se">\</span>
-<span class="s2">        patients are more likely to have a higher risk of heart disease.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="Creating-Hugging-Face-Dataset">
-<h2>Creating Hugging Face Dataset<a class="headerlink" href="#Creating-Hugging-Face-Dataset" title="Link to this heading">#</a></h2>
-<p>We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-<span class="n">dataset</span><span class="o">.</span><span class="n">cleanup_cache_files</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">cast_column</span><span class="p">(</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span> <span class="n">ClassLabel</span><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
-    <span class="n">train_size</span><span class="o">=</span><span class="n">TRAIN_SIZE</span><span class="p">,</span>
-    <span class="n">stratify_by_column</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span>
-    <span class="n">seed</span><span class="o">=</span><span class="n">RANDOM_SEED</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Model-Creation">
-<h2>Model Creation<a class="headerlink" href="#Model-Creation" title="Link to this heading">#</a></h2>
-<p>CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a SGD classifier to fit a logisitic regression model. The model configurations can be passed to <code class="docutils literal notranslate"><span class="pre">create_model</span></code> based on the sklearn parameters for SGDClassifier.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="s2">&quot;sgd_classifier&quot;</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">create_model</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s2">&quot;balanced&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Task-Creation">
-<h2>Task Creation<a class="headerlink" href="#Task-Creation" title="Link to this heading">#</a></h2>
-<p>We use Cyclops tasks to define our model’s task (in this case, heart failure prediction), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">heart_failure_prediction_task</span> <span class="o">=</span> <span class="n">BinaryTabularClassificationTask</span><span class="p">(</span>
-    <span class="p">{</span><span class="n">model_name</span><span class="p">:</span> <span class="n">model</span><span class="p">},</span>
-    <span class="n">task_features</span><span class="o">=</span><span class="n">features_list</span><span class="p">,</span>
-    <span class="n">task_target</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">heart_failure_prediction_task</span><span class="o">.</span><span class="n">list_models</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Training">
-<h2>Training<a class="headerlink" href="#Training" title="Link to this heading">#</a></h2>
-<p>If <code class="docutils literal notranslate"><span class="pre">best_model_params</span></code> is passed to the <code class="docutils literal notranslate"><span class="pre">train</span></code> method, the best model will be selected after the hyperparameter search. The parameters in <code class="docutils literal notranslate"><span class="pre">best_model_params</span></code> indicate the values to create the parameters grid.</p>
-<p>Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">best_model_params</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;alpha&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.0001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span>
-    <span class="s2">&quot;learning_rate&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;constant&quot;</span><span class="p">,</span> <span class="s2">&quot;optimal&quot;</span><span class="p">,</span> <span class="s2">&quot;invscaling&quot;</span><span class="p">,</span> <span class="s2">&quot;adaptive&quot;</span><span class="p">],</span>
-    <span class="s2">&quot;eta0&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.0001</span><span class="p">],</span>
-    <span class="s2">&quot;metric&quot;</span><span class="p">:</span> <span class="s2">&quot;roc_auc&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;method&quot;</span><span class="p">:</span> <span class="s2">&quot;grid&quot;</span><span class="p">,</span>
-<span class="p">}</span>
-
-<span class="n">heart_failure_prediction_task</span><span class="o">.</span><span class="n">train</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">best_model_params</span><span class="o">=</span><span class="n">best_model_params</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_params</span> <span class="o">=</span> <span class="n">heart_failure_prediction_task</span><span class="o">.</span><span class="n">list_models_params</span><span class="p">()[</span><span class="n">model_name</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">model_params</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Log the model parameters to the report.</strong></p>
-<p>We can add model parameters to the model card using the <code class="docutils literal notranslate"><span class="pre">log_model_parameters</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_model_parameters</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="n">model_params</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Prediction">
-<h2>Prediction<a class="headerlink" href="#Prediction" title="Link to this heading">#</a></h2>
-<p>The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">heart_failure_prediction_task</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">proba</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">only_predictions</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">prediction_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;y_prob&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">y_pred_i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">y_pred_i</span> <span class="ow">in</span> <span class="n">y_pred</span><span class="p">],</span>
-        <span class="s2">&quot;y_true&quot;</span><span class="p">:</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">][</span><span class="s2">&quot;outcome&quot;</span><span class="p">],</span>
-        <span class="s2">&quot;Sex&quot;</span><span class="p">:</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">][</span><span class="s2">&quot;Sex&quot;</span><span class="p">],</span>
-    <span class="p">}</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Evaluation">
-<h2>Evaluation<a class="headerlink" href="#Evaluation" title="Link to this heading">#</a></h2>
-<p>Evaluation is done using various evaluation metrics that provide different perspectives on the model’s predictive abilities i.e. standard performance metrics and fairness metrics.</p>
-<p>The standard performance metrics can be created using the <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code> object.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metric_names</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;binary_accuracy&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_precision&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_recall&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_f1_score&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_auroc&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_average_precision&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_roc_curve&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_precision_recall_curve&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric_name</span> <span class="ow">in</span> <span class="n">metric_names</span>
-<span class="p">]</span>
-<span class="n">metric_collection</span> <span class="o">=</span> <span class="n">MetricDict</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using <code class="docutils literal notranslate"><span class="pre">SliceSpec</span></code> objects.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">spec_list</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Age&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">30</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Age&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">70</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span><span class="s2">&quot;Sex&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">}},</span>
-<span class="p">]</span>
-<span class="n">slice_spec</span> <span class="o">=</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>A <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code> can also be defined for the fairness metrics.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">specificity</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;binary_specificity&quot;</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">sensitivity</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;binary_sensitivity&quot;</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="n">fpr</span> <span class="o">=</span> <span class="o">-</span><span class="n">specificity</span> <span class="o">+</span> <span class="mi">1</span>
-<span class="n">fnr</span> <span class="o">=</span> <span class="o">-</span><span class="n">sensitivity</span> <span class="o">+</span> <span class="mi">1</span>
-
-<span class="n">ber</span> <span class="o">=</span> <span class="p">(</span><span class="n">fpr</span> <span class="o">+</span> <span class="n">fnr</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
-
-<span class="n">fairness_metric_collection</span> <span class="o">=</span> <span class="n">MetricDict</span><span class="p">(</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Sensitivity&quot;</span><span class="p">:</span> <span class="n">sensitivity</span><span class="p">,</span>
-        <span class="s2">&quot;Specificity&quot;</span><span class="p">:</span> <span class="n">specificity</span><span class="p">,</span>
-        <span class="s2">&quot;BER&quot;</span><span class="p">:</span> <span class="n">ber</span><span class="p">,</span>
-    <span class="p">},</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The FairnessConfig helps in setting up and evaluating the fairness of the model predictions.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fairness_config</span> <span class="o">=</span> <span class="n">FairnessConfig</span><span class="p">(</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="n">fairness_metric_collection</span><span class="p">,</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># dataset is passed from the evaluator</span>
-    <span class="n">target_columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># target columns are passed from the evaluator</span>
-    <span class="n">groups</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Age&quot;</span><span class="p">],</span>
-    <span class="n">group_bins</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;Age&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">40</span><span class="p">,</span> <span class="mi">50</span><span class="p">]},</span>
-    <span class="n">group_base_values</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;Age&quot;</span><span class="p">:</span> <span class="mi">40</span><span class="p">},</span>
-    <span class="n">thresholds</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="p">,</span> <span class="n">dataset_with_preds</span> <span class="o">=</span> <span class="n">heart_failure_prediction_task</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="n">metric_collection</span><span class="p">,</span>
-    <span class="n">model_names</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">prediction_column_prefix</span><span class="o">=</span><span class="s2">&quot;preds&quot;</span><span class="p">,</span>
-    <span class="n">slice_spec</span><span class="o">=</span><span class="n">slice_spec</span><span class="p">,</span>
-    <span class="n">batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">fairness_config</span><span class="o">=</span><span class="n">fairness_config</span><span class="p">,</span>
-    <span class="n">override_fairness_metrics</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results_female</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">heart_failure_prediction_task</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="n">MetricDict</span><span class="p">(</span>
-        <span class="p">{</span>
-            <span class="s2">&quot;BinaryAccuracy&quot;</span><span class="p">:</span> <span class="n">create_metric</span><span class="p">(</span>
-                <span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;binary_accuracy&quot;</span><span class="p">,</span>
-                <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="p">),</span>
-        <span class="p">},</span>
-    <span class="p">),</span>
-    <span class="n">model_names</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">prediction_column_prefix</span><span class="o">=</span><span class="s2">&quot;preds&quot;</span><span class="p">,</span>
-    <span class="n">slice_spec</span><span class="o">=</span><span class="n">SliceSpec</span><span class="p">(</span>
-        <span class="p">[{</span><span class="s2">&quot;Sex&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;F&quot;</span><span class="p">}},</span> <span class="p">{</span><span class="s2">&quot;Sex&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">}}],</span> <span class="n">include_overall</span><span class="o">=</span><span class="kc">False</span>
-    <span class="p">),</span>
-    <span class="n">batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Log the performance metrics to the report.</strong></p>
-<p>We can add a performance metric to the model card using the <code class="docutils literal notranslate"><span class="pre">log_performance_metric</span></code> method, which expects a dictionary where the keys are in the following format: <code class="docutils literal notranslate"><span class="pre">slice_name/metric_name</span></code>. For instance, <code class="docutils literal notranslate"><span class="pre">overall/accuracy</span></code>.</p>
-<p>We first need to process the evaluation results to get the metrics in the right format.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;model_for_preds.</span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="n">results_flat</span> <span class="o">=</span> <span class="n">flatten_results_dict</span><span class="p">(</span>
-    <span class="n">results</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
-    <span class="n">remove_metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">results_female_flat</span> <span class="o">=</span> <span class="n">flatten_results_dict</span><span class="p">(</span>
-    <span class="n">results</span><span class="o">=</span><span class="n">results_female</span><span class="p">,</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="c1"># ruff: noqa: W505</span>
-<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">results_female_flat</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">split</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)</span>  <span class="c1"># noqa: PLW2901</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;BinaryPrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of predicted positive instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryRecall&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAccuracy&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of all instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAveragePrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryF1Score&quot;</span><span class="p">:</span> <span class="s2">&quot;The harmonic mean of precision and recall.&quot;</span><span class="p">,</span>
-    <span class="p">}</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">log_quantitative_analysis</span><span class="p">(</span>
-        <span class="s2">&quot;performance&quot;</span><span class="p">,</span>
-        <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
-        <span class="n">value</span><span class="o">=</span><span class="n">metric</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-        <span class="n">description</span><span class="o">=</span><span class="n">descriptions</span><span class="p">[</span><span class="n">name</span><span class="p">],</span>
-        <span class="n">metric_slice</span><span class="o">=</span><span class="n">split</span><span class="p">,</span>
-        <span class="n">pass_fail_thresholds</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
-        <span class="n">pass_fail_threshold_fns</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">),</span>
-    <span class="p">)</span>
-
-<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">results_flat</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">split</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)</span>  <span class="c1"># noqa: PLW2901</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;BinaryPrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of predicted positive instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryRecall&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAccuracy&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of all instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAveragePrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryF1Score&quot;</span><span class="p">:</span> <span class="s2">&quot;The harmonic mean of precision and recall.&quot;</span><span class="p">,</span>
-    <span class="p">}</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">log_quantitative_analysis</span><span class="p">(</span>
-        <span class="s2">&quot;performance&quot;</span><span class="p">,</span>
-        <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
-        <span class="n">value</span><span class="o">=</span><span class="n">metric</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-        <span class="n">description</span><span class="o">=</span><span class="n">descriptions</span><span class="p">[</span><span class="n">name</span><span class="p">],</span>
-        <span class="n">metric_slice</span><span class="o">=</span><span class="n">split</span><span class="p">,</span>
-        <span class="n">pass_fail_thresholds</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
-        <span class="n">pass_fail_threshold_fns</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">),</span>
-    <span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>We can also use the <code class="docutils literal notranslate"><span class="pre">ClassificationPlotter</span></code> to plot the performance metrics and the add the figure to the model card using the <code class="docutils literal notranslate"><span class="pre">log_plotly_figure</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plotter</span> <span class="o">=</span> <span class="n">ClassificationPlotter</span><span class="p">(</span><span class="n">task_type</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">])</span>
-<span class="n">plotter</span><span class="o">.</span><span class="n">set_template</span><span class="p">(</span><span class="s2">&quot;plotly_white&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># extracting the ROC curves and AUROC results for all the slices</span>
-<span class="n">roc_curves</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">aurocs</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">roc_curves</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># plotting the ROC curves for all the slices</span>
-<span class="n">roc_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">roc_curve_comparison</span><span class="p">(</span><span class="n">roc_curves</span><span class="p">,</span> <span class="n">aurocs</span><span class="o">=</span><span class="n">aurocs</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">roc_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;ROC Curve for Female Patients&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">roc_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># extracting the precision-recall curves and average precision results for all the slices</span>
-<span class="n">pr_curves</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">average_precisions</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryAveragePrecision&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">pr_curves</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># plotting the precision-recall curves for all the slices</span>
-<span class="n">pr_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">precision_recall_curve_comparison</span><span class="p">(</span>
-    <span class="n">pr_curves</span><span class="p">,</span>
-    <span class="n">auprcs</span><span class="o">=</span><span class="n">average_precisions</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">pr_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Precision-Recall Curve Comparison&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">pr_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the overall classification metric values.</span>
-<span class="n">overall_performance</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-    <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;overall&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-    <span class="k">if</span> <span class="n">metric_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the overall classification metric values.</span>
-<span class="n">overall_performance_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_value</span><span class="p">(</span>
-    <span class="n">overall_performance</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Overall Performance&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">overall_performance_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Overall Performance&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">overall_performance_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the metric values for all the slices.</span>
-<span class="n">slice_metrics</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="p">{</span>
-        <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-        <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">slice_results</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">metric_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-    <span class="p">}</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the metric values for all the slices.</span>
-<span class="n">slice_metrics_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_comparison_bar</span><span class="p">(</span><span class="n">slice_metrics</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">slice_metrics_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Slice Metric Comparison&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">slice_metrics_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the metric values for all the slices.</span>
-<span class="c1"># ROC curve components</span>
-<span class="n">pred_probs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dataset_with_preds</span><span class="p">[</span><span class="s2">&quot;preds.sgd_classifier&quot;</span><span class="p">])</span>
-<span class="n">true_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dataset_with_preds</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">])</span>
-<span class="n">roc_curve</span> <span class="o">=</span> <span class="n">binary_roc</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">pred_probs</span><span class="p">)</span>
-<span class="n">ppv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">roc_curve</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span>
-<span class="n">npv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">roc_curve</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span>
-
-<span class="c1"># Calculate PPV and NPV for each threshold</span>
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">threshold</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">roc_curve</span><span class="o">.</span><span class="n">thresholds</span><span class="p">):</span>
-    <span class="c1"># Calculate PPV and NPV</span>
-    <span class="n">ppv</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">binary_ppv</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">pred_probs</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">)</span>
-    <span class="n">npv</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">binary_npv</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">pred_probs</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">)</span>
-<span class="n">runway_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">threshperf</span><span class="p">(</span><span class="n">roc_curve</span><span class="p">,</span> <span class="n">ppv</span><span class="p">,</span> <span class="n">npv</span><span class="p">,</span> <span class="n">pred_probs</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">runway_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Threshold-Performance plot&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">runway_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p>We can also plot the calibration curve of the model on the test set</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">calibration_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">calibration</span><span class="p">(</span>
-    <span class="n">prediction_df</span><span class="p">,</span> <span class="n">y_true_col</span><span class="o">=</span><span class="s2">&quot;y_true&quot;</span><span class="p">,</span> <span class="n">y_prob_col</span><span class="o">=</span><span class="s2">&quot;y_prob&quot;</span><span class="p">,</span> <span class="n">group_col</span><span class="o">=</span><span class="s2">&quot;Sex&quot;</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">calibration_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Calibration plot&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">calibration_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Reformatting the fairness metrics</span>
-<span class="n">fairness_results</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;fairness&quot;</span><span class="p">])</span>
-<span class="n">fairness_metrics</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="c1"># remove the group size from the fairness results and add it to the slice name</span>
-<span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">fairness_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">group_size</span> <span class="o">=</span> <span class="n">slice_results</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;Group Size&quot;</span><span class="p">)</span>
-    <span class="n">fairness_metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">slice_name</span><span class="si">}</span><span class="s2"> (Size=</span><span class="si">{</span><span class="n">group_size</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_results</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the fairness metrics</span>
-<span class="n">fairness_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_comparison_scatter</span><span class="p">(</span>
-    <span class="n">fairness_metrics</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Fairness Metrics&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fairness_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Fairness Metrics&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;fairness analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fairness_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Report-Generation">
-<h2>Report Generation<a class="headerlink" href="#Report-Generation" title="Link to this heading">#</a></h2>
-<p>Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.</p>
-<p>Let’s start with populating the model details section, which includes the following fields by default: - description: A high-level description of the model and its usage for a general audience. - version: The version of the model. - owners: The individuals or organizations that own the model. - license: The license under which the model is made available. - citation: The citation for the model. - references: Links to resources that are relevant to the model. - path: The path to where the model
-is stored. - regulatory_requirements: The regulatory requirements that are relevant to the model.</p>
-<p>We can add additional fields to the model details section by passing a dictionary to the <code class="docutils literal notranslate"><span class="pre">log_from_dict</span></code> method and specifying the section name as <code class="docutils literal notranslate"><span class="pre">model_details</span></code>. You can also use the <code class="docutils literal notranslate"><span class="pre">log_descriptor</span></code> method to add a new field object with a <code class="docutils literal notranslate"><span class="pre">description</span></code> attribute to any section of the model card.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;Heart Failure Prediction Model&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;The model was trained on the Kaggle Heart Failure </span><span class="se">\</span>
-<span class="s2">        Prediction Dataset to predict risk of heart failure.&quot;</span><span class="p">,</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;model_details&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">report</span><span class="o">.</span><span class="n">log_version</span><span class="p">(</span>
-    <span class="n">version_str</span><span class="o">=</span><span class="s2">&quot;0.0.1&quot;</span><span class="p">,</span>
-    <span class="n">date</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">date</span><span class="o">.</span><span class="n">today</span><span class="p">()),</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Initial Release&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_owner</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;CyclOps Team&quot;</span><span class="p">,</span>
-    <span class="n">contact</span><span class="o">=</span><span class="s2">&quot;vectorinstitute.github.io/cyclops/&quot;</span><span class="p">,</span>
-    <span class="n">email</span><span class="o">=</span><span class="s2">&quot;cyclops@vectorinstitute.ai&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_license</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">&quot;Apache-2.0&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_reference</span><span class="p">(</span>
-    <span class="n">link</span><span class="o">=</span><span class="s2">&quot;https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html&quot;</span><span class="p">,</span>  <span class="c1"># noqa: E501</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Next, let’s populate the considerations section, which includes the following fields by default: - users: The intended users of the model. - use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases. - fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms. - ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This
-can be populated using the <code class="docutils literal notranslate"><span class="pre">log_risk</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;users&quot;</span><span class="p">:</span> <span class="p">[</span>
-            <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;Hospitals&quot;</span><span class="p">},</span>
-            <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;Clinicians&quot;</span><span class="p">},</span>
-        <span class="p">],</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;considerations&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_user</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;ML Engineers&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_use_case</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Predicting risk of heart failure.&quot;</span><span class="p">,</span>
-    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;primary&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_use_case</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Predicting risk of pathologies and conditions other</span><span class="se">\</span>
-<span class="s2">    than heart failure.&quot;</span><span class="p">,</span>
-    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;out-of-scope&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_fairness_assessment</span><span class="p">(</span>
-    <span class="n">affected_group</span><span class="o">=</span><span class="s2">&quot;sex, age&quot;</span><span class="p">,</span>
-    <span class="n">benefit</span><span class="o">=</span><span class="s2">&quot;Improved health outcomes for patients.&quot;</span><span class="p">,</span>
-    <span class="n">harm</span><span class="o">=</span><span class="s2">&quot;Biased predictions for patients in certain groups (e.g. older patients) </span><span class="se">\</span>
-<span class="s2">        may lead to worse health outcomes.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;We will monitor the performance of the model on these groups </span><span class="se">\</span>
-<span class="s2">        and retrain the model if the performance drops below a certain threshold.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_risk</span><span class="p">(</span>
-    <span class="n">risk</span><span class="o">=</span><span class="s2">&quot;The model may be used to make decisions that affect the health of patients.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;The model should be continuously monitored for performance </span><span class="se">\</span>
-<span class="s2">        and retrained if the performance drops below a certain threshold.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Once the model card is populated, you can generate the report using the <code class="docutils literal notranslate"><span class="pre">export</span></code> method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named <code class="docutils literal notranslate"><span class="pre">cyclops_reports</span></code> in the current working directory. You can change the path by passing a <code class="docutils literal notranslate"><span class="pre">output_dir</span></code> argument when instantiating the <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> class.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-
-<span class="n">synthetic_timestamps</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span>
-    <span class="n">start</span><span class="o">=</span><span class="s2">&quot;1/1/2020&quot;</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s2">&quot;D&quot;</span>
-<span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
-
-
-<span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">overview</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="n">report_path</span> <span class="o">=</span> <span class="n">report</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
-    <span class="n">output_filename</span><span class="o">=</span><span class="s2">&quot;heart_failure_report_periodic.html&quot;</span><span class="p">,</span>
-    <span class="n">synthetic_timestamp</span><span class="o">=</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-    <span class="n">last_n_evals</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
-<span class="n">metric_save</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="mi">1</span><span class="p">:])):</span>
-    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-        <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">quantitative_analysis</span><span class="o">.</span><span class="n">performance_metrics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="n">metric_save</span><span class="p">,</span>
-        <span class="p">)</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">overview</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">quantitative_analysis</span><span class="o">.</span><span class="n">performance_metrics</span><span class="p">:</span>
-        <span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span>
-            <span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span>
-            <span class="mi">0</span><span class="p">,</span>
-            <span class="mi">1</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">metric</span><span class="o">.</span><span class="n">tests</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">passed</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">&gt;=</span> <span class="mf">0.7</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="n">metrics</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">quantitative_analysis</span><span class="o">.</span><span class="n">performance_metrics</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">metric</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;BinaryAccuracy&quot;</span> <span class="ow">and</span> <span class="n">metric</span><span class="o">.</span><span class="n">slice</span> <span class="o">==</span> <span class="s2">&quot;Age:[30 - 50)&quot;</span><span class="p">:</span>
-                <span class="n">metric_save</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">metric</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">metrics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">metric</span><span class="p">)</span>
-        <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">quantitative_analysis</span><span class="o">.</span><span class="n">performance_metrics</span> <span class="o">=</span> <span class="n">metrics</span>
-    <span class="n">report_path</span> <span class="o">=</span> <span class="n">report</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
-        <span class="n">output_filename</span><span class="o">=</span><span class="s2">&quot;heart_failure_report_periodic.html&quot;</span><span class="p">,</span>
-        <span class="n">synthetic_timestamp</span><span class="o">=</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span>
-    <span class="p">)</span>
-    <span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
-<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="s2">&quot;./cyclops_report&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>You can view the generated HTML <a class="reference external" href="./heart_failure_report_periodic.html">report</a>.</p>
-</section>
-</section>
-
-        </article>
-      </div>
-      <footer>
-        
-        <div class="related-pages">
-          <a class="next-page" href="../mimiciv/mortality_prediction.html">
-              <div class="page-info">
-                <div class="context">
-                  <span>Next</span>
-                </div>
-                <div class="title">Mortality Prediction</div>
-              </div>
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-            </a>
-          <a class="prev-page" href="../../tutorials_use_cases.html">
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-              <div class="page-info">
-                <div class="context">
-                  <span>Previous</span>
-                </div>
-                
-                <div class="title">Example use cases</div>
-                
-              </div>
-            </a>
-        </div>
-        <div class="bottom-of-page">
-          <div class="left-details">
-            <div class="copyright">
-                Copyright &#169; 2024, Vector AI Engineering
-            </div>
-            <!--
-            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-            
-            <a href="https://github.com/pradyunsg/furo">Furo</a>
-            
-            -->
-          </div>
-          <div class="right-details">
-            <div class="icons">
-              <a class="muted-link " href="https://github.com/VectorInstitute/cyclops" aria-label="GitHub">
-                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
-                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
-                </svg>
-            </a>
-              
-            </div>
-          </div>
-        </div>
-        <div class="footer-container" style="text-align: center;">
-          <div class="footer-small-text">
-              <br> All <a href="https://github.com/VectorInstitute/cyclops">source code for cyclops</a> is freely available under the terms of an <a href="https://opensource.org/licenses/Apache-2.0">Apache-2.0</a> license.
-          </div>
-
-          <div class="footer-small-text">
-            This work is made possible due to the data obtained from the <a href="https://www.geminimedicine.ca/">GEMINI study</a>.
-          </div>
-
-          <div class="logo">
-            <a class="logo" href="https://vectorinstitute.ai/">
-              <span class="logo"></span>
-              <img alt="Logo of the Vector Institute" style="width:90px; max-width:90px;" src="../../_static/logos/vector_logo.png" />
-            </a>
-            <a class="logo" href="https://www.geminimedicine.ca/">
-              <span class="logo"></span>
-              <img alt="Logo of GEMINI" style="width:90px; max-width:90px;" src="../../_static/logos/gemini_logo.png" />
-            </a>
-          </div>
-        </div>
-        
-      </footer>
-    </div>
-    <aside class="toc-drawer">
-      
-      
-      <div class="toc-sticky toc-scroll">
-        <div class="toc-title-container">
-          <span class="toc-title">
-            On this page
-          </span>
-        </div>
-        <div class="toc-tree-container">
-          <div class="toc-tree">
-            <ul>
-<li><a class="reference internal" href="#">Heart Failure Prediction</a><ul>
-<li><a class="reference internal" href="#Import-Libraries">Import Libraries</a></li>
-<li><a class="reference internal" href="#Constants">Constants</a></li>
-<li><a class="reference internal" href="#Data-Loading">Data Loading</a><ul>
-<li><a class="reference internal" href="#Sex-values">Sex values</a></li>
-<li><a class="reference internal" href="#Age-distribution">Age distribution</a></li>
-<li><a class="reference internal" href="#Outcome-distribution">Outcome distribution</a></li>
-<li><a class="reference internal" href="#Identifying-feature-types">Identifying feature types</a></li>
-<li><a class="reference internal" href="#Creating-data-preprocessors">Creating data preprocessors</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#Creating-Hugging-Face-Dataset">Creating Hugging Face Dataset</a></li>
-<li><a class="reference internal" href="#Model-Creation">Model Creation</a></li>
-<li><a class="reference internal" href="#Task-Creation">Task Creation</a></li>
-<li><a class="reference internal" href="#Training">Training</a></li>
-<li><a class="reference internal" href="#Prediction">Prediction</a></li>
-<li><a class="reference internal" href="#Evaluation">Evaluation</a></li>
-<li><a class="reference internal" href="#Report-Generation">Report Generation</a></li>
-</ul>
-</li>
-</ul>
-
-          </div>
-        </div>
-      </div>
-      
-      
-    </aside>
-  </div>
-</div></body>
-</html>
\ No newline at end of file
diff --git a/api/tutorials/kaggle/heart_failure_prediction.ipynb b/api/tutorials/kaggle/heart_failure_prediction.ipynb
deleted file mode 100644
index 1f987623f..000000000
--- a/api/tutorials/kaggle/heart_failure_prediction.ipynb
+++ /dev/null
@@ -1,1398 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Heart Failure Prediction\n",
-    "\n",
-    "This notebook showcases heart failure prediction on a [popular Kaggle dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) using CyclOps. The task is formulated as a binary classification task, where we predict the probability that a patient will have heart failure."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"Heart failure prediction.\"\"\"\n",
-    "\n",
-    "import copy\n",
-    "import inspect\n",
-    "import shutil\n",
-    "from datetime import date\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "from datasets import Dataset\n",
-    "from datasets.features import ClassLabel\n",
-    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
-    "\n",
-    "from cyclops.data.df.feature import TabularFeatures\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate.fairness import FairnessConfig  # noqa: E402\n",
-    "from cyclops.evaluate.metrics import create_metric\n",
-    "from cyclops.evaluate.metrics.experimental.functional import (\n",
-    "    binary_npv,\n",
-    "    binary_ppv,\n",
-    "    binary_roc,\n",
-    ")\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.models.catalog import create_model\n",
-    "from cyclops.report import ModelCardReport\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter\n",
-    "from cyclops.report.utils import flatten_results_dict\n",
-    "from cyclops.tasks import BinaryTabularClassificationTask\n",
-    "from cyclops.utils.file import join, load_dataframe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "CyclOps offers a package for documentation of the model through a model report. The `ModelCardReport` class is used to populate and generate the model report as an HTML file. The model report has the following sections:\n",
-    "\n",
-    "- Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).\n",
-    "- Datasets: High level statistics of the training data, including changes in distribution over time.\n",
-    "- Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.\n",
-    "- Fairness Analysis: This section contains the fairness metrics of the model.\n",
-    "- Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.\n",
-    "- Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.\n",
-    "- Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.\n",
-    "\n",
-    "We will use this to document the model development process as we go along and generate the model report at the end.\n",
-    "\n",
-    "`The model report tool is a work in progress and is subject to change.`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "DATA_DIR = \"./data\"\n",
-    "RANDOM_SEED = 85\n",
-    "NAN_THRESHOLD = 0.75\n",
-    "TRAIN_SIZE = 0.8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Loading\n",
-    "\n",
-    "Before starting, make sure to install the Kaggle API by running `pip install kaggle`. To use the Kaggle API, you need to sign up for a Kaggle account at https://www.kaggle.com. Then go to the 'Account' tab of your user profile (`https://www.kaggle.com/<username>/account`) and select 'Create API Token'. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location `~/.kaggle/kaggle.json` on your machine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "api = KaggleApi()\n",
-    "api.authenticate()\n",
-    "api.dataset_download_files(\n",
-    "    \"fedesoriano/heart-failure-prediction\",\n",
-    "    path=DATA_DIR,\n",
-    "    unzip=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "df = load_dataframe(join(DATA_DIR, \"heart.csv\"), file_format=\"csv\")\n",
-    "df = df.reset_index()\n",
-    "df[\"ID\"] = df.index\n",
-    "print(df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sex values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"Sex\")\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    title=\"Sex Distribution\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Sex Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###  Age distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(df, x=\"Age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Age Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Outcome distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "df[\"outcome\"] = df[\"HeartDisease\"].astype(\"int\")\n",
-    "df = df.drop(columns=[\"HeartDisease\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.pie(df, names=\"outcome\")\n",
-    "fig.update_traces(textinfo=\"percent+label\")\n",
-    "fig.update_layout(title_text=\"Outcome Distribution\")\n",
-    "fig.update_traces(\n",
-    "    hovertemplate=\"Outcome: %{label}<br>Count: \\\n",
-    "    %{value}<br>Percent: %{percent}\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Outcome Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "class_counts = df[\"outcome\"].value_counts()\n",
-    "class_ratio = class_counts[0] / class_counts[1]\n",
-    "print(class_ratio, class_counts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "print(df.columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From all the features in the dataset, we select 20 of them which was reported by [Li et al.](https://pubmed.ncbi.nlm.nih.gov/34301649/)  to be the most important features in this classification task. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "features_list = [\n",
-    "    \"Age\",\n",
-    "    \"Sex\",\n",
-    "    \"ChestPainType\",\n",
-    "    \"RestingBP\",\n",
-    "    \"Cholesterol\",\n",
-    "    \"FastingBS\",\n",
-    "    \"RestingECG\",\n",
-    "    \"MaxHR\",\n",
-    "    \"ExerciseAngina\",\n",
-    "    \"Oldpeak\",\n",
-    "    \"ST_Slope\",\n",
-    "]\n",
-    "\n",
-    "features_list = sorted(features_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Identifying feature types\n",
-    "\n",
-    "Cyclops `TabularFeatures` class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "tab_features = TabularFeatures(\n",
-    "    data=df.reset_index(),\n",
-    "    features=features_list,\n",
-    "    by=\"ID\",\n",
-    "    targets=\"outcome\",\n",
-    ")\n",
-    "print(tab_features.types)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating data preprocessors\n",
-    "\n",
-    "We create a data preprocessor using sklearn's ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"mean\")), (\"scaler\", MinMaxScaler())],\n",
-    ")\n",
-    "\n",
-    "binary_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"most_frequent\"))],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_features = sorted((tab_features.features_by_type(\"numeric\")))\n",
-    "numeric_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in numeric_features\n",
-    "]\n",
-    "print(numeric_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "binary_features = sorted(tab_features.features_by_type(\"binary\"))\n",
-    "binary_features.remove(\"outcome\")\n",
-    "ordinal_features = sorted(tab_features.features_by_type(\"ordinal\"))\n",
-    "binary_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in binary_features\n",
-    "]\n",
-    "ordinal_indices = [\n",
-    "    df[features_list].columns.get_loc(column) for column in ordinal_features\n",
-    "]\n",
-    "print(binary_features, ordinal_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "preprocessor = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        (\"num\", numeric_transformer, numeric_indices),\n",
-    "        (\n",
-    "            \"onehot\",\n",
-    "            OneHotEncoder(handle_unknown=\"ignore\"),\n",
-    "            binary_indices + ordinal_indices,\n",
-    "        ),\n",
-    "    ],\n",
-    "    remainder=\"passthrough\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's document the dataset in the model card. This can be done using the `log_dataset` method, which takes the following arguments:\n",
-    "- description: A description of the dataset.\n",
-    "- citation: The citation for the dataset.\n",
-    "- link: A link to a resource for the dataset.\n",
-    "- license_id: The SPDX license identifier for the dataset.\n",
-    "- version: The version of the dataset.\n",
-    "- features: A list of features in the dataset.\n",
-    "- split: The split of the dataset (train, test, validation, etc.).\n",
-    "- sensitive_features: A list of sensitive features used to train/evaluate the model.\n",
-    "- sensitive_feature_justification: A justification for the sensitive features used to train/evaluate the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_dataset(\n",
-    "    description=\"\"\"This dataset was created by combining different datasets\n",
-    "    already available independently but not combined before. In this dataset,\n",
-    "    5 heart datasets are combined over 11 common features. Every dataset used\n",
-    "    can be found under the Index of heart disease datasets from UCI\n",
-    "    Machine Learning Repository on the following link:\n",
-    "    https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/.\"\"\",\n",
-    "    citation=inspect.cleandoc(\n",
-    "        \"\"\"\n",
-    "        @misc{fedesoriano,\n",
-    "          title={Heart Failure Prediction Dataset.},\n",
-    "          author={Fedesoriano, F},\n",
-    "          year={2021},\n",
-    "          publisher={Kaggle}\n",
-    "        }\n",
-    "    \"\"\",\n",
-    "    ),\n",
-    "    link=\"\"\"\n",
-    "    https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction\n",
-    "    \"\"\",\n",
-    "    license_id=\"CC0-1.0\",\n",
-    "    version=\"Version 1\",\n",
-    "    features=features_list,\n",
-    "    sensitive_features=[\"Sex\", \"Age\"],\n",
-    "    sensitive_feature_justification=\"Demographic information like age and gender \\\n",
-    "        often have a strong correlation with health outcomes. For example, older \\\n",
-    "        patients are more likely to have a higher risk of heart disease.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating Hugging Face Dataset\n",
-    "\n",
-    "We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = Dataset.from_pandas(df)\n",
-    "dataset.cleanup_cache_files()\n",
-    "print(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = dataset.cast_column(\"outcome\", ClassLabel(num_classes=2))\n",
-    "dataset = dataset.train_test_split(\n",
-    "    train_size=TRAIN_SIZE,\n",
-    "    stratify_by_column=\"outcome\",\n",
-    "    seed=RANDOM_SEED,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model Creation\n",
-    "\n",
-    "CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a SGD classifier to fit a logisitic regression model. The model configurations can be passed to `create_model` based on the sklearn parameters for SGDClassifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_name = \"sgd_classifier\"\n",
-    "model = create_model(model_name, random_state=123, verbose=0, class_weight=\"balanced\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Task Creation\n",
-    "\n",
-    "We use Cyclops tasks to define our model's task (in this case, heart failure prediction), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "heart_failure_prediction_task = BinaryTabularClassificationTask(\n",
-    "    {model_name: model},\n",
-    "    task_features=features_list,\n",
-    "    task_target=\"outcome\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "heart_failure_prediction_task.list_models()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training\n",
-    "\n",
-    "If `best_model_params` is passed to the `train` method, the best model will be selected after the hyperparameter search. The parameters in `best_model_params` indicate the values to create the parameters grid.\n",
-    "\n",
-    "Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "best_model_params = {\n",
-    "    \"alpha\": [0.0001, 0.001, 0.01, 0.1, 1, 10, 100],\n",
-    "    \"learning_rate\": [\"constant\", \"optimal\", \"invscaling\", \"adaptive\"],\n",
-    "    \"eta0\": [0.1, 0.01, 0.001, 0.0001],\n",
-    "    \"metric\": \"roc_auc\",\n",
-    "    \"method\": \"grid\",\n",
-    "}\n",
-    "\n",
-    "heart_failure_prediction_task.train(\n",
-    "    dataset[\"train\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    best_model_params=best_model_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_params = heart_failure_prediction_task.list_models_params()[model_name]\n",
-    "print(model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the model parameters to the report.**\n",
-    "\n",
-    "We can add model parameters to the model card using the `log_model_parameters` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_model_parameters(params=model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prediction\n",
-    "\n",
-    "The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "y_pred = heart_failure_prediction_task.predict(\n",
-    "    dataset[\"test\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    proba=True,\n",
-    "    only_predictions=True,\n",
-    ")\n",
-    "prediction_df = pd.DataFrame(\n",
-    "    {\n",
-    "        \"y_prob\": [y_pred_i[1] for y_pred_i in y_pred],\n",
-    "        \"y_true\": dataset[\"test\"][\"outcome\"],\n",
-    "        \"Sex\": dataset[\"test\"][\"Sex\"],\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluation\n",
-    "\n",
-    "Evaluation is done using various evaluation metrics that provide different perspectives on the model's predictive abilities i.e. standard performance metrics and fairness metrics.\n",
-    "\n",
-    "The standard performance metrics can be created using the `MetricDict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_auroc\",\n",
-    "    \"binary_average_precision\",\n",
-    "    \"binary_roc_curve\",\n",
-    "    \"binary_precision_recall_curve\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using `SliceSpec` objects. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"Age\": {\n",
-    "            \"min_value\": 30,\n",
-    "            \"max_value\": 50,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"Age\": {\n",
-    "            \"min_value\": 50,\n",
-    "            \"max_value\": 70,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\"Sex\": {\"value\": \"M\"}},\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `MetricDict` can also be defined for the fairness metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "specificity = create_metric(metric_name=\"binary_specificity\", experimental=True)\n",
-    "sensitivity = create_metric(metric_name=\"binary_sensitivity\", experimental=True)\n",
-    "\n",
-    "fpr = -specificity + 1\n",
-    "fnr = -sensitivity + 1\n",
-    "\n",
-    "ber = (fpr + fnr) / 2\n",
-    "\n",
-    "fairness_metric_collection = MetricDict(\n",
-    "    {\n",
-    "        \"Sensitivity\": sensitivity,\n",
-    "        \"Specificity\": specificity,\n",
-    "        \"BER\": ber,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The FairnessConfig helps in setting up and evaluating the fairness of the model predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fairness_config = FairnessConfig(\n",
-    "    metrics=fairness_metric_collection,\n",
-    "    dataset=None,  # dataset is passed from the evaluator\n",
-    "    target_columns=None,  # target columns are passed from the evaluator\n",
-    "    groups=[\"Age\"],\n",
-    "    group_bins={\"Age\": [40, 50]},\n",
-    "    group_base_values={\"Age\": 40},\n",
-    "    thresholds=[0.5],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "results, dataset_with_preds = heart_failure_prediction_task.evaluate(\n",
-    "    dataset=dataset[\"test\"],\n",
-    "    metrics=metric_collection,\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=slice_spec,\n",
-    "    batch_size=-1,\n",
-    "    fairness_config=fairness_config,\n",
-    "    override_fairness_metrics=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_female, _ = heart_failure_prediction_task.evaluate(\n",
-    "    dataset=dataset[\"test\"],\n",
-    "    metrics=MetricDict(\n",
-    "        {\n",
-    "            \"BinaryAccuracy\": create_metric(\n",
-    "                metric_name=\"binary_accuracy\",\n",
-    "                experimental=True,\n",
-    "            ),\n",
-    "        },\n",
-    "    ),\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=SliceSpec(\n",
-    "        [{\"Sex\": {\"value\": \"F\"}}, {\"Sex\": {\"value\": \"M\"}}], include_overall=False\n",
-    "    ),\n",
-    "    batch_size=-1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the performance metrics to the report.**\n",
-    "\n",
-    "We can add a performance metric to the model card using the `log_performance_metric` method, which expects a dictionary where the keys are in the following format: `slice_name/metric_name`. For instance, `overall/accuracy`. \n",
-    "\n",
-    "We first need to process the evaluation results to get the metrics in the right format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_name = f\"model_for_preds.{model_name}\"\n",
-    "results_flat = flatten_results_dict(\n",
-    "    results=results,\n",
-    "    remove_metrics=[\"BinaryROC\", \"BinaryPrecisionRecallCurve\"],\n",
-    "    model_name=model_name,\n",
-    ")\n",
-    "results_female_flat = flatten_results_dict(\n",
-    "    results=results_female,\n",
-    "    model_name=model_name,\n",
-    ")\n",
-    "# ruff: noqa: W505\n",
-    "for name, metric in results_female_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )\n",
-    "\n",
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the `ClassificationPlotter` to plot the performance metrics and the add the figure to the model card using the `log_plotly_figure` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the ROC curves and AUROC results for all the slices\n",
-    "roc_curves = {\n",
-    "    slice_name: slice_results[\"BinaryROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "aurocs = {\n",
-    "    slice_name: slice_results[\"BinaryAUROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "roc_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the ROC curves for all the slices\n",
-    "roc_plot = plotter.roc_curve_comparison(roc_curves, aurocs=aurocs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=roc_plot,\n",
-    "    caption=\"ROC Curve for Female Patients\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "roc_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the precision-recall curves and average precision results for all the slices\n",
-    "pr_curves = {\n",
-    "    slice_name: slice_results[\"BinaryPrecisionRecallCurve\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "average_precisions = {\n",
-    "    slice_name: slice_results[\"BinaryAveragePrecision\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "pr_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the precision-recall curves for all the slices\n",
-    "pr_plot = plotter.precision_recall_curve_comparison(\n",
-    "    pr_curves,\n",
-    "    auprcs=average_precisions,\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=pr_plot,\n",
-    "    caption=\"Precision-Recall Curve Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "pr_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the overall classification metric values.\n",
-    "overall_performance = {\n",
-    "    metric_name: metric_value\n",
-    "    for metric_name, metric_value in results[model_name][\"overall\"].items()\n",
-    "    if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the overall classification metric values.\n",
-    "overall_performance_plot = plotter.metrics_value(\n",
-    "    overall_performance,\n",
-    "    title=\"Overall Performance\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=overall_performance_plot,\n",
-    "    caption=\"Overall Performance\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "overall_performance_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=slice_metrics_plot,\n",
-    "    caption=\"Slice Metric Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "# ROC curve components\n",
-    "pred_probs = np.array(dataset_with_preds[\"preds.sgd_classifier\"])\n",
-    "true_labels = np.array(dataset_with_preds[\"outcome\"])\n",
-    "roc_curve = binary_roc(true_labels, pred_probs)\n",
-    "ppv = np.zeros_like(roc_curve.thresholds)\n",
-    "npv = np.zeros_like(roc_curve.thresholds)\n",
-    "\n",
-    "# Calculate PPV and NPV for each threshold\n",
-    "for i, threshold in enumerate(roc_curve.thresholds):\n",
-    "    # Calculate PPV and NPV\n",
-    "    ppv[i] = binary_ppv(true_labels, pred_probs, threshold=threshold)\n",
-    "    npv[i] = binary_npv(true_labels, pred_probs, threshold=threshold)\n",
-    "runway_plot = plotter.threshperf(roc_curve, ppv, npv, pred_probs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=runway_plot,\n",
-    "    caption=\"Threshold-Performance plot\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "runway_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot the calibration curve of the model on the test set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "calibration_plot = plotter.calibration(\n",
-    "    prediction_df, y_true_col=\"y_true\", y_prob_col=\"y_prob\", group_col=\"Sex\"\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=calibration_plot,\n",
-    "    caption=\"Calibration plot\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "calibration_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reformatting the fairness metrics\n",
-    "fairness_results = copy.deepcopy(results[\"fairness\"])\n",
-    "fairness_metrics = {}\n",
-    "# remove the group size from the fairness results and add it to the slice name\n",
-    "for slice_name, slice_results in fairness_results.items():\n",
-    "    group_size = slice_results.pop(\"Group Size\")\n",
-    "    fairness_metrics[f\"{slice_name} (Size={group_size})\"] = slice_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the fairness metrics\n",
-    "fairness_plot = plotter.metrics_comparison_scatter(\n",
-    "    fairness_metrics,\n",
-    "    title=\"Fairness Metrics\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fairness_plot,\n",
-    "    caption=\"Fairness Metrics\",\n",
-    "    section_name=\"fairness analysis\",\n",
-    ")\n",
-    "fairness_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Report Generation\n",
-    "\n",
-    "Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.\n",
-    "\n",
-    "\n",
-    "Let's start with populating the model details section, which includes the following fields by default:\n",
-    "- description: A high-level description of the model and its usage for a general audience.\n",
-    "- version: The version of the model.\n",
-    "- owners: The individuals or organizations that own the model.\n",
-    "- license: The license under which the model is made available.\n",
-    "- citation: The citation for the model.\n",
-    "- references: Links to resources that are relevant to the model.\n",
-    "- path: The path to where the model is stored.\n",
-    "- regulatory_requirements: The regulatory requirements that are relevant to the model.\n",
-    "\n",
-    "We can add additional fields to the model details section by passing a dictionary to the `log_from_dict` method and specifying the section name as `model_details`. You can also use the `log_descriptor` method to add a new field object with a `description` attribute to any section of the model card."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"Heart Failure Prediction Model\",\n",
-    "        \"description\": \"The model was trained on the Kaggle Heart Failure \\\n",
-    "        Prediction Dataset to predict risk of heart failure.\",\n",
-    "    },\n",
-    "    section_name=\"model_details\",\n",
-    ")\n",
-    "\n",
-    "report.log_version(\n",
-    "    version_str=\"0.0.1\",\n",
-    "    date=str(date.today()),\n",
-    "    description=\"Initial Release\",\n",
-    ")\n",
-    "report.log_owner(\n",
-    "    name=\"CyclOps Team\",\n",
-    "    contact=\"vectorinstitute.github.io/cyclops/\",\n",
-    "    email=\"cyclops@vectorinstitute.ai\",\n",
-    ")\n",
-    "report.log_license(identifier=\"Apache-2.0\")\n",
-    "report.log_reference(\n",
-    "    link=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\",  # noqa: E501\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, let's populate the considerations section, which includes the following fields by default:\n",
-    "- users: The intended users of the model.\n",
-    "- use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases.\n",
-    "- fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms.\n",
-    "- ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This can be populated using the  `log_risk` method.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"users\": [\n",
-    "            {\"description\": \"Hospitals\"},\n",
-    "            {\"description\": \"Clinicians\"},\n",
-    "        ],\n",
-    "    },\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_user(description=\"ML Engineers\")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of heart failure.\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting risk of pathologies and conditions other\\\n",
-    "    than heart failure.\",\n",
-    "    kind=\"out-of-scope\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"sex, age\",\n",
-    "    benefit=\"Improved health outcomes for patients.\",\n",
-    "    harm=\"Biased predictions for patients in certain groups (e.g. older patients) \\\n",
-    "        may lead to worse health outcomes.\",\n",
-    "    mitigation_strategy=\"We will monitor the performance of the model on these groups \\\n",
-    "        and retrain the model if the performance drops below a certain threshold.\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"The model may be used to make decisions that affect the health of patients.\",\n",
-    "    mitigation_strategy=\"The model should be continuously monitored for performance \\\n",
-    "        and retrained if the performance drops below a certain threshold.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the model card is populated, you can generate the report using the `export` method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named `cyclops_reports` in the current working directory. You can change the path by passing a `output_dir` argument when instantiating the `ModelCardReport` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "np.random.seed(42)\n",
-    "\n",
-    "synthetic_timestamps = pd.date_range(\n",
-    "    start=\"1/1/2020\", periods=10, freq=\"D\"\n",
-    ").values.astype(str)\n",
-    "\n",
-    "\n",
-    "report._model_card.overview = None\n",
-    "report_path = report.export(\n",
-    "    output_filename=\"heart_failure_report_periodic.html\",\n",
-    "    synthetic_timestamp=synthetic_timestamps[0],\n",
-    "    last_n_evals=3,\n",
-    ")\n",
-    "\n",
-    "shutil.copy(f\"{report_path}\", \".\")\n",
-    "metric_save = None\n",
-    "for i in range(len(synthetic_timestamps[1:])):\n",
-    "    if i == 3:\n",
-    "        report._model_card.quantitative_analysis.performance_metrics.append(\n",
-    "            metric_save,\n",
-    "        )\n",
-    "    report._model_card.overview = None\n",
-    "    for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "        metric.value = np.clip(\n",
-    "            metric.value + np.random.normal(0, 0.1),\n",
-    "            0,\n",
-    "            1,\n",
-    "        )\n",
-    "        metric.tests[0].passed = bool(metric.value >= 0.7)\n",
-    "    if i == 2:\n",
-    "        metrics = []\n",
-    "        for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "            if metric.type == \"BinaryAccuracy\" and metric.slice == \"Age:[30 - 50)\":\n",
-    "                metric_save = copy.deepcopy(metric)\n",
-    "            else:\n",
-    "                metrics.append(metric)\n",
-    "        report._model_card.quantitative_analysis.performance_metrics = metrics\n",
-    "    report_path = report.export(\n",
-    "        output_filename=\"heart_failure_report_periodic.html\",\n",
-    "        synthetic_timestamp=synthetic_timestamps[i + 1],\n",
-    "    )\n",
-    "    shutil.copy(f\"{report_path}\", \".\")\n",
-    "shutil.rmtree(\"./cyclops_report\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "You can view the generated HTML [report](./heart_failure_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/api/tutorials/mimiciv/mortality_prediction.html b/api/tutorials/mimiciv/mortality_prediction.html
deleted file mode 100644
index e1a3bb886..000000000
--- a/api/tutorials/mimiciv/mortality_prediction.html
+++ /dev/null
@@ -1,1517 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en">
-  <head><meta charset="utf-8"/>
-    <meta name="viewport" content="width=device-width,initial-scale=1"/>
-    <meta name="color-scheme" content="light dark">
-    <meta name="google-site-verification" content="i0qQRaR9OA3tSz_9tDocdcXGY27Ox_cy4FrvTHD2C_0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-
-    
-      <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Prolonged Length of Stay Prediction" href="../synthea/los_prediction.html" /><link rel="prev" title="Heart Failure Prediction" href="../kaggle/heart_failure_prediction.html" />
-
-    <link rel="shortcut icon" href="../../_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
-
-    
-    <meta name="msvalidate.01" content="8D2A5032F006424CAF54C0DDCD16F666" />
-      
-        <title>Mortality Prediction - cyclops documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
-    <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="../../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
-    <link rel="stylesheet" type="text/css" href="../../_static/nbsphinx-code-cells.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/css/cyclops.css?v=9f20b50b" />
-    
-    
-
-
-<style>
-  body {
-    --color-code-background: #f8f8f8;
-  --color-code-foreground: black;
-  --color-card-background: #5e88f2;
-  
-  }
-  @media not print {
-    body[data-theme="dark"] {
-      --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-    }
-    @media (prefers-color-scheme: dark) {
-      body:not([data-theme="light"]) {
-        --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-      }
-    }
-  }
-</style><script src="../../_static/documentation_options.js?v=5929fcd5"></script>
-    <script src="../../_static/doctools.js?v=888ff710"></script>
-    <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="../../_static/scripts/furo.js?v=32e29ea5"></script>
-    <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
-    <script src="../../_static/copybutton.js?v=ccdb6887"></script>
-    <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-    <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script src="../../_static/require.min.js?v=53367eec"></script>
-    <script src="../../_static/custom.js?v=275847af"></script>
-    
-    <script src="https://kit.fontawesome.com/2c1f516901.js" crossorigin="anonymous"></script>
-
-  </head>
-  <body>
-    
-    <script>
-      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
-    </script>
-    
-
-<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
-  <symbol id="svg-toc" viewBox="0 0 24 24">
-    <title>Contents</title>
-    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
-      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-menu" viewBox="0 0 24 24">
-    <title>Menu</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
-      <line x1="3" y1="12" x2="21" y2="12"></line>
-      <line x1="3" y1="6" x2="21" y2="6"></line>
-      <line x1="3" y1="18" x2="21" y2="18"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
-    <title>Expand</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
-      <polyline points="9 18 15 12 9 6"></polyline>
-    </svg>
-  </symbol>
-  <symbol id="svg-sun" viewBox="0 0 24 24">
-    <title>Light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
-      <circle cx="12" cy="12" r="5"></circle>
-      <line x1="12" y1="1" x2="12" y2="3"></line>
-      <line x1="12" y1="21" x2="12" y2="23"></line>
-      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
-      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
-      <line x1="1" y1="12" x2="3" y2="12"></line>
-      <line x1="21" y1="12" x2="23" y2="12"></line>
-      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
-      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon" viewBox="0 0 24 24">
-    <title>Dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
-      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
-    </svg>
-  </symbol>
-  <symbol id="svg-sun-half" viewBox="0 0 24 24">
-    <title>Auto light/dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-      <circle cx="12" cy="12" r="9" />
-      <path d="M13 12h5" />
-      <path d="M13 15h4" />
-      <path d="M13 18h1" />
-      <path d="M13 9h4" />
-      <path d="M13 6h1" />
-    </svg>
-  </symbol>
-</svg>
-
-<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
-<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
-<label class="overlay sidebar-overlay" for="__navigation">
-  <div class="visually-hidden">Hide navigation sidebar</div>
-</label>
-<label class="overlay toc-overlay" for="__toc">
-  <div class="visually-hidden">Hide table of contents sidebar</div>
-</label>
-
-
-
-<div class="page">
-  <header class="mobile-header">
-    <div class="header-left">
-      <label class="nav-overlay-icon" for="__navigation">
-        <div class="visually-hidden">Toggle site navigation sidebar</div>
-        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
-      </label>
-    </div>
-    <div class="header-center">
-      <a href="../../index.html"><div class="brand">cyclops  documentation</div></a>
-    </div>
-    <div class="header-right">
-      <div class="theme-toggle-container theme-toggle-header">
-        <button class="theme-toggle">
-          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-        </button>
-      </div>
-      <label class="toc-overlay-icon toc-header-icon" for="__toc">
-        <div class="visually-hidden">Toggle table of contents sidebar</div>
-        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-      </label>
-    </div>
-  </header>
-  <aside class="sidebar-drawer">
-    <div class="sidebar-container">
-      
-      <div class="sidebar-sticky"><a class="sidebar-brand" href="../../index.html">
-  
-  <div class="sidebar-logo-container">
-    <img class="sidebar-logo" src="../../_static/cyclops_logo-dark.png" alt="Logo"/>
-  </div>
-  
-  
-</a><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
-  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
-  <input type="hidden" name="check_keywords" value="yes">
-  <input type="hidden" name="area" value="default">
-</form>
-<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <ul class="current">
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l2 current has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="../kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../developing.html">🧑🏿‍💻 Developing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing to cyclops</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../api.html">API Reference</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of API Reference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.data.html">cyclops.data</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of cyclops.data</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.html">cyclops.data.slicer</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of cyclops.data.slicer</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.compound_filter.html">cyclops.data.slicer.compound_filter</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html">cyclops.data.slicer.filter_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html">cyclops.data.slicer.filter_non_null</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_range.html">cyclops.data.slicer.filter_range</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html">cyclops.data.slicer.filter_string_contains</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_value.html">cyclops.data.slicer.filter_value</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.is_datetime.html">cyclops.data.slicer.is_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.overall.html">cyclops.data.slicer.overall</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html">cyclops.data.slicer.SliceSpec</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.html">cyclops.data.aggregate</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of cyclops.data.aggregate</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html">cyclops.data.aggregate.tabular_as_aggregated</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html">cyclops.data.aggregate.timestamp_ffill_agg</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html">cyclops.data.aggregate.Aggregator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.html">cyclops.data.features.medical_image</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of cyclops.data.features.medical_image</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html">cyclops.data.features.medical_image.MedicalImage</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.tasks.html">cyclops.tasks</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of cyclops.tasks</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.html">cyclops.tasks.classification</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of cyclops.tasks.classification</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html">cyclops.tasks.classification.BinaryTabularClassificationTask</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html">cyclops.tasks.classification.MultilabelImageClassificationTask</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/evaluator.html">Evaluator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/fairness_evaluator.html">Fairness Evaluator</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/metrics.html">Evaluation Metrics</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of Evaluation Metrics</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/accuracy.html">Accuracy</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/auroc.html">AUROC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/average_precision.html">Average Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f1score.html">F1-Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f_beta.html">F-beta Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision.html">Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision_recall_curve.html">PrecisionRecallCurve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/recall.html">Recall</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/roc.html">ROC Curve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/sensitivity.html">Sensitivity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/specificity.html">Specificity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/stat_scores.html">StatScores</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.monitor.html">cyclops.monitor</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of cyclops.monitor</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.html">cyclops.monitor.clinical_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of cyclops.monitor.clinical_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html">cyclops.monitor.clinical_applicator.ClinicalShiftApplicator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html">cyclops.monitor.synthetic_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of cyclops.monitor.synthetic_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html">cyclops.monitor.synthetic_applicator.binary_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html">cyclops.monitor.synthetic_applicator.feature_association_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html">cyclops.monitor.synthetic_applicator.feature_swap_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html">cyclops.monitor.synthetic_applicator.gaussian_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html">cyclops.monitor.synthetic_applicator.knockout_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html">cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.report.html">cyclops.report</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of cyclops.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.html">cyclops.report.report</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of cyclops.report.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.ModelCardReport.html">cyclops.report.report.ModelCardReport</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-</div>
-</div>
-
-      </div>
-      
-    </div>
-  </aside>
-  <div class="main">
-    <div class="content">
-      <div class="article-container">
-        <a href="#" class="back-to-top muted-link">
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
-            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
-          </svg>
-          <span>Back to top</span>
-        </a>
-        <div class="content-icon-container">
-          
-<div class="theme-toggle-container theme-toggle-content">
-            <button class="theme-toggle">
-              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-            </button>
-          </div>
-          <label class="toc-overlay-icon toc-content-icon" for="__toc">
-            <div class="visually-hidden">Toggle table of contents sidebar</div>
-            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-          </label>
-        </div>
-        <article role="main">
-          <section id="Mortality-Prediction">
-<h1>Mortality Prediction<a class="headerlink" href="#Mortality-Prediction" title="Link to this heading">#</a></h1>
-<p>This notebook showcases mortality prediction on the <a class="reference external" href="https://physionet.org/content/mimiciv/2.0">MIMICIV</a> dataset using CyclOps. The task is formulated as a binary classification task, whether the patient will die within the next N days. The prediction can be made after M number of days after admission. For example, if N = 14 and M = 1, we are predicting risk of patient mortality within 14 days of admission after considering 24 hours of data after admission.</p>
-<section id="Import-Libraries">
-<h2>Import Libraries<a class="headerlink" href="#Import-Libraries" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;Mortality Prediction.&quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">shutil</span>
-<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">date</span>
-
-<span class="kn">import</span> <span class="nn">cycquery.ops</span> <span class="k">as</span> <span class="nn">qo</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
-<span class="kn">import</span> <span class="nn">plotly.graph_objects</span> <span class="k">as</span> <span class="nn">go</span>
-<span class="kn">from</span> <span class="nn">cycquery</span> <span class="kn">import</span> <span class="n">MIMICIVQuerier</span>
-<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">Dataset</span>
-<span class="kn">from</span> <span class="nn">datasets.features</span> <span class="kn">import</span> <span class="n">ClassLabel</span>
-<span class="kn">from</span> <span class="nn">imblearn.over_sampling</span> <span class="kn">import</span> <span class="n">SMOTE</span>
-<span class="kn">from</span> <span class="nn">imblearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span> <span class="k">as</span> <span class="n">ImbPipeline</span>
-<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
-<span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
-<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">OneHotEncoder</span>
-
-<span class="kn">from</span> <span class="nn">cyclops.data.aggregate</span> <span class="kn">import</span> <span class="n">RESTRICT_TIMESTAMP</span><span class="p">,</span> <span class="n">Aggregator</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.clean</span> <span class="kn">import</span> <span class="n">normalize_names</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.df.feature</span> <span class="kn">import</span> <span class="n">TabularFeatures</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.slicer</span> <span class="kn">import</span> <span class="n">SliceSpec</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.fairness</span> <span class="kn">import</span> <span class="n">FairnessConfig</span>  <span class="c1"># noqa: E402</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics</span> <span class="kn">import</span> <span class="n">create_metric</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics.experimental.metric_dict</span> <span class="kn">import</span> <span class="n">MetricDict</span>
-<span class="kn">from</span> <span class="nn">cyclops.models.catalog</span> <span class="kn">import</span> <span class="n">create_model</span>
-<span class="kn">from</span> <span class="nn">cyclops.report</span> <span class="kn">import</span> <span class="n">ModelCardReport</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.plot.classification</span> <span class="kn">import</span> <span class="n">ClassificationPlotter</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.utils</span> <span class="kn">import</span> <span class="n">flatten_results_dict</span>
-<span class="kn">from</span> <span class="nn">cyclops.tasks</span> <span class="kn">import</span> <span class="n">BinaryTabularClassificationTask</span>
-<span class="kn">from</span> <span class="nn">cyclops.utils.common</span> <span class="kn">import</span> <span class="n">add_years_approximate</span>
-</pre></div>
-</div>
-</div>
-<p>CyclOps offers a package for documentation of the model through a model report. The <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> class is used to populate and generate the model report as an HTML file. The model report has the following sections:</p>
-<ul class="simple">
-<li><p>Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).</p></li>
-<li><p>Datasets: High level statistics of the training data, including changes in distribution over time.</p></li>
-<li><p>Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.</p></li>
-<li><p>Fairness Analysis: This section contains the fairness metrics of the model.</p></li>
-<li><p>Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.</p></li>
-<li><p>Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.</p></li>
-<li><p>Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.</p></li>
-</ul>
-<p>We will use this to document the model development process as we go along and generate the model report at the end.</p>
-<p><code class="docutils literal notranslate"><span class="pre">The</span> <span class="pre">model</span> <span class="pre">report</span> <span class="pre">tool</span> <span class="pre">is</span> <span class="pre">a</span> <span class="pre">work</span> <span class="pre">in</span> <span class="pre">progress</span> <span class="pre">and</span> <span class="pre">is</span> <span class="pre">subject</span> <span class="pre">to</span> <span class="pre">change.</span></code></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">ModelCardReport</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Constants">
-<h2>Constants<a class="headerlink" href="#Constants" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">M</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">N</span> <span class="o">=</span> <span class="mi">14</span>
-<span class="n">NAN_THRESHOLD</span> <span class="o">=</span> <span class="mf">0.25</span>
-<span class="n">TRAIN_SIZE</span> <span class="o">=</span> <span class="mf">0.8</span>
-<span class="n">RANDOM_SEED</span> <span class="o">=</span> <span class="mi">12</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Data-Querying-&amp;-Processing">
-<h2>Data Querying &amp; Processing<a class="headerlink" href="#Data-Querying-&-Processing" title="Link to this heading">#</a></h2>
-<section id="Compute-mortality-(labels)">
-<h3>Compute mortality (labels)<a class="headerlink" href="#Compute-mortality-(labels)" title="Link to this heading">#</a></h3>
-<ol class="arabic simple">
-<li><p>Get encounters</p></li>
-<li><p>Filter out encounters less than M days</p></li>
-<li><p>Set label = 1 for encounters where deathtime is within N days after admission</p></li>
-<li><p>Get lab events</p></li>
-<li><p>Aggregate them by computing mean, merge with encounter data</p></li>
-</ol>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">querier</span> <span class="o">=</span> <span class="n">MIMICIVQuerier</span><span class="p">(</span>
-    <span class="n">dbms</span><span class="o">=</span><span class="s2">&quot;postgresql&quot;</span><span class="p">,</span>
-    <span class="n">port</span><span class="o">=</span><span class="mi">5432</span><span class="p">,</span>
-    <span class="n">host</span><span class="o">=</span><span class="s2">&quot;localhost&quot;</span><span class="p">,</span>
-    <span class="n">database</span><span class="o">=</span><span class="s2">&quot;mimiciv-2.0&quot;</span><span class="p">,</span>
-    <span class="n">user</span><span class="o">=</span><span class="s2">&quot;postgres&quot;</span><span class="p">,</span>
-    <span class="n">password</span><span class="o">=</span><span class="s2">&quot;pwd&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">get_encounters</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get encounters data.&quot;&quot;&quot;</span>
-    <span class="n">patients</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">patients</span><span class="p">()</span>
-    <span class="n">encounters</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">mimiciv_hosp</span><span class="o">.</span><span class="n">admissions</span><span class="p">()</span>
-    <span class="n">drop_op</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">Drop</span><span class="p">(</span>
-        <span class="p">[</span><span class="s2">&quot;language&quot;</span><span class="p">,</span> <span class="s2">&quot;marital_status&quot;</span><span class="p">,</span> <span class="s2">&quot;edregtime&quot;</span><span class="p">,</span> <span class="s2">&quot;edouttime&quot;</span><span class="p">],</span>
-    <span class="p">)</span>
-    <span class="n">encounters</span> <span class="o">=</span> <span class="n">encounters</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">drop_op</span><span class="p">)</span>
-    <span class="n">patient_encounters</span> <span class="o">=</span> <span class="n">patients</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">encounters</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;subject_id&quot;</span><span class="p">)</span>
-    <span class="n">patient_encounters</span> <span class="o">=</span> <span class="n">patient_encounters</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
-    <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;admittime&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">year</span>
-        <span class="o">-</span> <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;anchor_year&quot;</span><span class="p">]</span>
-        <span class="o">+</span> <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;anchor_age&quot;</span><span class="p">]</span>
-    <span class="p">)</span>
-    <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;admittime&quot;</span><span class="p">,</span> <span class="s2">&quot;dischtime&quot;</span><span class="p">,</span> <span class="s2">&quot;deathtime&quot;</span><span class="p">]:</span>
-        <span class="n">patient_encounters</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">add_years_approximate</span><span class="p">(</span>
-            <span class="n">patient_encounters</span><span class="p">[</span><span class="n">col</span><span class="p">],</span>
-            <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;anchor_year_difference&quot;</span><span class="p">],</span>
-        <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">patient_encounters</span><span class="p">[</span>
-        <span class="p">[</span>
-            <span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;admittime&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;dischtime&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;deathtime&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;anchor_age&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;age&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;gender&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;anchor_year_difference&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;admission_location&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;admission_type&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;insurance&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;hospital_expire_flag&quot;</span><span class="p">,</span>
-        <span class="p">]</span>
-    <span class="p">]</span>
-
-
-<span class="k">def</span> <span class="nf">compute_mortality_outcome</span><span class="p">(</span><span class="n">patient_encounters</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compute mortality outcome.&quot;&quot;&quot;</span>
-    <span class="c1"># Drop encounters ending in death which don&#39;t have a death timestamp</span>
-    <span class="n">invalid</span> <span class="o">=</span> <span class="p">(</span><span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;hospital_expire_flag&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span>
-        <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;deathtime&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span>
-    <span class="p">)</span>
-    <span class="n">patient_encounters</span> <span class="o">=</span> <span class="n">patient_encounters</span><span class="p">[</span><span class="o">~</span><span class="n">invalid</span><span class="p">]</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Encounters with death flag but no death timestamp: </span><span class="si">{</span><span class="n">invalid</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-    <span class="c1"># Drop encounters which are shorter than M days</span>
-    <span class="n">invalid</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;dischtime&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;admittime&quot;</span><span class="p">]</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span> <span class="o">&lt;</span> <span class="n">M</span>
-    <span class="n">patient_encounters</span> <span class="o">=</span> <span class="n">patient_encounters</span><span class="p">[</span><span class="o">~</span><span class="n">invalid</span><span class="p">]</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Encounters shorter than </span><span class="si">{</span><span class="n">M</span><span class="si">}</span><span class="s2"> days: </span><span class="si">{</span><span class="n">invalid</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-    <span class="c1"># Death timestamp is within (&lt;=) N days of admission</span>
-    <span class="n">valid</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;deathtime&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;admittime&quot;</span><span class="p">]</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span> <span class="o">&lt;=</span> <span class="n">N</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Encounters with death timestamp within </span><span class="si">{</span><span class="n">N</span><span class="si">}</span><span class="s2"> days: </span><span class="si">{</span><span class="n">valid</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-    <span class="c1"># (Died in hospital) &amp; (Death timestamp is defined)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">patient_encounters</span><span class="p">))</span>
-    <span class="n">patient_encounters</span><span class="p">[</span><span class="s2">&quot;mortality_outcome&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
-        <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">patient_encounters</span><span class="p">),</span>
-        <span class="n">index</span><span class="o">=</span><span class="n">patient_encounters</span><span class="o">.</span><span class="n">index</span><span class="p">,</span>
-        <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;int64[pyarrow]&quot;</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">patient_encounters</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">valid</span><span class="p">,</span> <span class="s2">&quot;mortality_outcome&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-    <span class="nb">print</span><span class="p">(</span>
-        <span class="sa">f</span><span class="s2">&quot;Encounters with mortality outcome for the model: </span><span class="si">{</span><span class="n">patient_encounters</span><span class="p">[</span><span class="s1">&#39;mortality_outcome&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">patient_encounters</span>
-
-
-<span class="k">def</span> <span class="nf">get_labevents</span><span class="p">(</span><span class="n">patient_encounters</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get labevents data.&quot;&quot;&quot;</span>
-    <span class="n">labevents</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">labevents</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">index_col</span><span class="o">=</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span> <span class="n">batch_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">process_labevents</span><span class="p">(</span><span class="n">labevents</span><span class="p">,</span> <span class="n">patient_encounters</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Process labevents before aggregation.&quot;&quot;&quot;</span>
-        <span class="c1"># Reverse deidentified dating</span>
-        <span class="n">labevents</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span>
-            <span class="n">patient_encounters</span><span class="p">[</span>
-                <span class="p">[</span>
-                    <span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span>
-                    <span class="s2">&quot;anchor_year_difference&quot;</span><span class="p">,</span>
-                <span class="p">]</span>
-            <span class="p">],</span>
-            <span class="n">labevents</span><span class="p">,</span>
-            <span class="n">on</span><span class="o">=</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">labevents</span><span class="p">[</span><span class="s2">&quot;charttime&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">add_years_approximate</span><span class="p">(</span>
-            <span class="n">labevents</span><span class="p">[</span><span class="s2">&quot;charttime&quot;</span><span class="p">],</span>
-            <span class="n">labevents</span><span class="p">[</span><span class="s2">&quot;anchor_year_difference&quot;</span><span class="p">],</span>
-        <span class="p">)</span>
-        <span class="n">labevents</span> <span class="o">=</span> <span class="n">labevents</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;anchor_year_difference&quot;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="c1"># Pre-processing</span>
-        <span class="n">labevents</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">normalize_names</span><span class="p">(</span><span class="n">labevents</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">])</span>
-        <span class="n">labevents</span><span class="p">[</span><span class="s2">&quot;category&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">normalize_names</span><span class="p">(</span><span class="n">labevents</span><span class="p">[</span><span class="s2">&quot;category&quot;</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="n">labevents</span>
-
-    <span class="n">start_timestamps</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="n">patient_encounters</span><span class="p">[[</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span> <span class="s2">&quot;admittime&quot;</span><span class="p">]]</span>
-        <span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">)</span>
-        <span class="o">.</span><span class="n">rename</span><span class="p">({</span><span class="s2">&quot;admittime&quot;</span><span class="p">:</span> <span class="n">RESTRICT_TIMESTAMP</span><span class="p">},</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="p">)</span>
-    <span class="n">mean_aggregator</span> <span class="o">=</span> <span class="n">Aggregator</span><span class="p">(</span>
-        <span class="n">aggfuncs</span><span class="o">=</span><span class="p">{</span>
-            <span class="s2">&quot;valuenum&quot;</span><span class="p">:</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
-        <span class="p">},</span>
-        <span class="n">window_duration</span><span class="o">=</span><span class="n">M</span> <span class="o">*</span> <span class="mi">24</span><span class="p">,</span>
-        <span class="n">window_start_time</span><span class="o">=</span><span class="n">start_timestamps</span><span class="p">,</span>
-        <span class="n">timestamp_col</span><span class="o">=</span><span class="s2">&quot;charttime&quot;</span><span class="p">,</span>
-        <span class="n">time_by</span><span class="o">=</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span>
-        <span class="n">agg_by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span> <span class="s2">&quot;label&quot;</span><span class="p">],</span>
-    <span class="p">)</span>
-    <span class="n">means_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">batch_num</span><span class="p">,</span> <span class="n">labevents_batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">labevents</span><span class="p">):</span>
-        <span class="n">labevents_batch</span> <span class="o">=</span> <span class="n">process_labevents</span><span class="p">(</span>  <span class="c1"># noqa: PLW2901</span>
-            <span class="n">labevents_batch</span><span class="p">,</span>
-            <span class="n">patient_encounters</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">means</span> <span class="o">=</span> <span class="n">mean_aggregator</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span>
-            <span class="n">labevents_batch</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">means</span> <span class="o">=</span> <span class="n">means</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
-        <span class="n">means</span> <span class="o">=</span> <span class="n">means</span><span class="o">.</span><span class="n">pivot</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s2">&quot;valuenum&quot;</span><span class="p">)</span>
-        <span class="n">means</span> <span class="o">=</span> <span class="n">means</span><span class="o">.</span><span class="n">add_prefix</span><span class="p">(</span><span class="s2">&quot;lab_&quot;</span><span class="p">)</span>
-        <span class="n">means</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span>
-            <span class="n">patient_encounters</span><span class="p">[</span>
-                <span class="p">[</span>
-                    <span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span>
-                    <span class="s2">&quot;mortality_outcome&quot;</span><span class="p">,</span>
-                    <span class="s2">&quot;age&quot;</span><span class="p">,</span>
-                    <span class="s2">&quot;gender&quot;</span><span class="p">,</span>
-                    <span class="s2">&quot;admission_location&quot;</span><span class="p">,</span>
-                <span class="p">]</span>
-            <span class="p">],</span>
-            <span class="n">means</span><span class="p">,</span>
-            <span class="n">on</span><span class="o">=</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">means_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">means_df</span><span class="p">,</span> <span class="n">means</span><span class="p">])</span>
-        <span class="k">if</span> <span class="n">batch_num</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">break</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Processing batch </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">batch_num</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">means_df</span>
-
-
-<span class="k">def</span> <span class="nf">run_query</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Run query.&quot;&quot;&quot;</span>
-    <span class="n">cohort</span> <span class="o">=</span> <span class="n">get_encounters</span><span class="p">()</span>
-    <span class="n">cohort</span> <span class="o">=</span> <span class="n">compute_mortality_outcome</span><span class="p">(</span><span class="n">cohort</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">get_labevents</span><span class="p">(</span><span class="n">cohort</span><span class="p">)</span>
-
-
-<span class="n">cohort</span> <span class="o">=</span> <span class="n">run_query</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="Data-Inspection-and-Preprocessing">
-<h2>Data Inspection and Preprocessing<a class="headerlink" href="#Data-Inspection-and-Preprocessing" title="Link to this heading">#</a></h2>
-<section id="Drop-NaNs-based-on-the-NAN_THRESHOLD">
-<h3>Drop NaNs based on the <code class="docutils literal notranslate"><span class="pre">NAN_THRESHOLD</span></code><a class="headerlink" href="#Drop-NaNs-based-on-the-NAN_THRESHOLD" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">null_counts</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()[</span><span class="n">cohort</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[</span><span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">null_counts</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">null_counts</span><span class="o">.</span><span class="n">values</span><span class="p">)])</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Number of Null Values per Column&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Columns&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Number of Null Values&quot;</span><span class="p">,</span>
-    <span class="n">height</span><span class="o">=</span><span class="mi">600</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<p>We can use the log_plotly_figure method to add the figure to a section of the report. One can specify whether the figure should be interactive or not by setting the <code class="docutils literal notranslate"><span class="pre">interactive</span></code> parameter to <code class="docutils literal notranslate"><span class="pre">True</span></code> or <code class="docutils literal notranslate"><span class="pre">False</span></code> respectively. The default value is <code class="docutils literal notranslate"><span class="pre">True</span></code>. This also affects the final size of the report. If the figure is interactive, the size of the report will be larger than if the figure is not interactive.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Number of Null Values per Column&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">thresh_nan</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">NAN_THRESHOLD</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">cohort</span><span class="p">))</span>
-<span class="n">cohort</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">thresh</span><span class="o">=</span><span class="n">thresh_nan</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Outcome-distribution">
-<h3>Outcome distribution<a class="headerlink" href="#Outcome-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">cohort</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="s2">&quot;mortality_outcome&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span><span class="n">textinfo</span><span class="o">=</span><span class="s2">&quot;percent+label&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span><span class="n">title_text</span><span class="o">=</span><span class="s2">&quot;Outcome Distribution&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span>
-    <span class="n">hovertemplate</span><span class="o">=</span><span class="s2">&quot;Outcome: %</span><span class="si">{label}</span><span class="s2">&lt;br&gt;Count: </span><span class="se">\</span>
-<span class="s2">    %</span><span class="si">{value}</span><span class="s2">&lt;br&gt;Percent: %</span><span class="si">{percent}</span><span class="s2">&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Outcome Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The data is heavily unbalanced.</span>
-<span class="n">class_counts</span> <span class="o">=</span> <span class="n">cohort</span><span class="p">[</span><span class="s2">&quot;mortality_outcome&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
-<span class="n">class_ratio</span> <span class="o">=</span> <span class="n">class_counts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">class_counts</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">class_ratio</span><span class="p">,</span> <span class="n">class_counts</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Gender-distribution">
-<h3>Gender distribution<a class="headerlink" href="#Gender-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">cohort</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="s2">&quot;gender&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Gender Distribution&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Gender Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Age-distribution">
-<h3>Age distribution<a class="headerlink" href="#Age-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">cohort</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">,</span>
-    <span class="n">bargap</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Identifying-feature-types">
-<h3>Identifying feature types<a class="headerlink" href="#Identifying-feature-types" title="Link to this heading">#</a></h3>
-<p>Cyclops <code class="docutils literal notranslate"><span class="pre">TabularFeatures</span></code> class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">features_list</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">cohort</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> <span class="o">-</span> <span class="p">{</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span> <span class="s2">&quot;mortality_outcome&quot;</span><span class="p">}</span>
-<span class="n">features_list</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">features_list</span><span class="p">)</span>
-<span class="n">tab_features</span> <span class="o">=</span> <span class="n">TabularFeatures</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="n">cohort</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(),</span>
-    <span class="n">features</span><span class="o">=</span><span class="n">features_list</span><span class="p">,</span>
-    <span class="n">by</span><span class="o">=</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">,</span>
-    <span class="n">targets</span><span class="o">=</span><span class="s2">&quot;mortality_outcome&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">tab_features</span><span class="o">.</span><span class="n">types</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Creating-data-preprocessors">
-<h3>Creating data preprocessors<a class="headerlink" href="#Creating-data-preprocessors" title="Link to this heading">#</a></h3>
-<p>We create a data preprocessor using sklearn’s ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numeric_transformer</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span>
-    <span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;imputer&quot;</span><span class="p">,</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)),</span> <span class="p">(</span><span class="s2">&quot;scaler&quot;</span><span class="p">,</span> <span class="n">MinMaxScaler</span><span class="p">())],</span>
-<span class="p">)</span>
-<span class="n">binary_transformer</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span>
-    <span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;imputer&quot;</span><span class="p">,</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;most_frequent&quot;</span><span class="p">))],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numeric_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">((</span><span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;numeric&quot;</span><span class="p">)))</span>
-<span class="n">numeric_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">cohort</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">numeric_features</span>
-<span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">numeric_features</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">binary_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;binary&quot;</span><span class="p">))</span>
-<span class="n">ordinal_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;ordinal&quot;</span><span class="p">))</span>
-<span class="n">binary_features</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s2">&quot;mortality_outcome&quot;</span><span class="p">)</span>
-<span class="n">binary_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">cohort</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">binary_features</span>
-<span class="p">]</span>
-<span class="n">ordinal_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">cohort</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">ordinal_features</span>
-<span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">binary_features</span><span class="p">,</span> <span class="n">ordinal_features</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">preprocessor</span> <span class="o">=</span> <span class="n">ColumnTransformer</span><span class="p">(</span>
-    <span class="n">transformers</span><span class="o">=</span><span class="p">[</span>
-        <span class="p">(</span><span class="s2">&quot;num&quot;</span><span class="p">,</span> <span class="n">numeric_transformer</span><span class="p">,</span> <span class="n">numeric_indices</span><span class="p">),</span>
-        <span class="p">(</span>
-            <span class="s2">&quot;onehot&quot;</span><span class="p">,</span>
-            <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">handle_unknown</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">),</span>
-            <span class="n">binary_indices</span> <span class="o">+</span> <span class="n">ordinal_indices</span><span class="p">,</span>
-        <span class="p">),</span>
-    <span class="p">],</span>
-    <span class="n">remainder</span><span class="o">=</span><span class="s2">&quot;passthrough&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">preprocessor_pipeline</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">(</span><span class="s2">&quot;preprocessor&quot;</span><span class="p">,</span> <span class="n">preprocessor</span><span class="p">),</span>
-    <span class="p">(</span><span class="s2">&quot;oversampling&quot;</span><span class="p">,</span> <span class="n">SMOTE</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_SEED</span><span class="p">)),</span>
-<span class="p">]</span>
-<span class="n">preprocessor_pipeline</span> <span class="o">=</span> <span class="n">ImbPipeline</span><span class="p">(</span><span class="n">preprocessor_pipeline</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="Creating-Hugging-Face-Dataset">
-<h2>Creating Hugging Face Dataset<a class="headerlink" href="#Creating-Hugging-Face-Dataset" title="Link to this heading">#</a></h2>
-<p>We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cohort</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;hadm_id&quot;</span><span class="p">])</span>
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">cohort</span><span class="p">)</span>
-<span class="n">dataset</span><span class="o">.</span><span class="n">cleanup_cache_files</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">cast_column</span><span class="p">(</span><span class="s2">&quot;mortality_outcome&quot;</span><span class="p">,</span> <span class="n">ClassLabel</span><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
-    <span class="n">train_size</span><span class="o">=</span><span class="n">TRAIN_SIZE</span><span class="p">,</span>
-    <span class="n">stratify_by_column</span><span class="o">=</span><span class="s2">&quot;mortality_outcome&quot;</span><span class="p">,</span>
-    <span class="n">seed</span><span class="o">=</span><span class="n">RANDOM_SEED</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Model-Creation">
-<h2>Model Creation<a class="headerlink" href="#Model-Creation" title="Link to this heading">#</a></h2>
-<p>CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a XGBoost classifier to fit a logisitic regression model. The model configurations can be passed to <code class="docutils literal notranslate"><span class="pre">create_model</span></code> based on the parameters for XGBClassifier.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="s2">&quot;xgb_classifier&quot;</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">create_model</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Task-Creation">
-<h2>Task Creation<a class="headerlink" href="#Task-Creation" title="Link to this heading">#</a></h2>
-<p>We use Cyclops tasks to define our model’s task (in this case, BinaryTabularClassificationTask), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mortality_task</span> <span class="o">=</span> <span class="n">BinaryTabularClassificationTask</span><span class="p">(</span>
-    <span class="p">{</span><span class="n">model_name</span><span class="p">:</span> <span class="n">model</span><span class="p">},</span>
-    <span class="n">task_features</span><span class="o">=</span><span class="n">features_list</span><span class="p">,</span>
-    <span class="n">task_target</span><span class="o">=</span><span class="s2">&quot;mortality_outcome&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">mortality_task</span><span class="o">.</span><span class="n">list_models</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Training">
-<h2>Training<a class="headerlink" href="#Training" title="Link to this heading">#</a></h2>
-<p>If <code class="docutils literal notranslate"><span class="pre">best_model_params</span></code> is passed to the <code class="docutils literal notranslate"><span class="pre">train</span></code> method, the best model will be selected after the hyperparameter search. The parameters in <code class="docutils literal notranslate"><span class="pre">best_model_params</span></code> indicate the values to create the parameters grid.</p>
-<p>Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">best_model_params</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;n_estimators&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">500</span><span class="p">],</span>
-    <span class="s2">&quot;learning_rate&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">],</span>
-    <span class="s2">&quot;max_depth&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
-    <span class="s2">&quot;reg_lambda&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span>
-    <span class="s2">&quot;colsample_bytree&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
-    <span class="s2">&quot;gamma&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span>
-    <span class="s2">&quot;method&quot;</span><span class="p">:</span> <span class="s2">&quot;random&quot;</span><span class="p">,</span>
-<span class="p">}</span>
-<span class="n">mortality_task</span><span class="o">.</span><span class="n">train</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor_pipeline</span><span class="p">,</span>
-    <span class="n">best_model_params</span><span class="o">=</span><span class="n">best_model_params</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_params</span> <span class="o">=</span> <span class="n">mortality_task</span><span class="o">.</span><span class="n">list_models_params</span><span class="p">()[</span><span class="n">model_name</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">model_params</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Log the model parameters to the report.</strong></p>
-<p>We can add model parameters to the model card using the <code class="docutils literal notranslate"><span class="pre">log_model_parameters</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_model_parameters</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="n">model_params</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Prediction">
-<h2>Prediction<a class="headerlink" href="#Prediction" title="Link to this heading">#</a></h2>
-<p>The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">mortality_task</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">proba</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">only_predictions</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_pred</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Evaluation">
-<h2>Evaluation<a class="headerlink" href="#Evaluation" title="Link to this heading">#</a></h2>
-<p>Evaluation is done using various evaluation metrics that provide different perspectives on the model’s predictive abilities i.e. standard performance metrics and fairness metrics.</p>
-<p>The standard performance metrics can be created using the <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code> object.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metric_names</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;binary_accuracy&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_precision&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_recall&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_f1_score&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_auroc&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_average_precision&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_roc_curve&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_precision_recall_curve&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric_name</span> <span class="ow">in</span> <span class="n">metric_names</span>
-<span class="p">]</span>
-<span class="n">metric_collection</span> <span class="o">=</span> <span class="n">MetricDict</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using <code class="docutils literal notranslate"><span class="pre">SliceSpec</span></code> objects.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">spec_list</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">20</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">80</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span><span class="s2">&quot;gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">}},</span>
-    <span class="p">{</span><span class="s2">&quot;gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;F&quot;</span><span class="p">}},</span>
-<span class="p">]</span>
-<span class="n">slice_spec</span> <span class="o">=</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>A <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code> can also be defined for the fairness metrics.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">specificity</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;binary_specificity&quot;</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">sensitivity</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;binary_sensitivity&quot;</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">fpr</span> <span class="o">=</span> <span class="o">-</span><span class="n">specificity</span> <span class="o">+</span> <span class="mi">1</span>  <span class="c1"># __rsub__ is not implemented for metrics</span>
-<span class="n">fnr</span> <span class="o">=</span> <span class="o">-</span><span class="n">sensitivity</span> <span class="o">+</span> <span class="mi">1</span>
-<span class="n">ber</span> <span class="o">=</span> <span class="p">(</span><span class="n">fpr</span> <span class="o">+</span> <span class="n">fnr</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
-<span class="n">fairness_metric_collection</span> <span class="o">=</span> <span class="n">MetricDict</span><span class="p">(</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Sensitivity&quot;</span><span class="p">:</span> <span class="n">sensitivity</span><span class="p">,</span>
-        <span class="s2">&quot;Specificity&quot;</span><span class="p">:</span> <span class="n">specificity</span><span class="p">,</span>
-        <span class="s2">&quot;BER&quot;</span><span class="p">:</span> <span class="n">ber</span><span class="p">,</span>
-    <span class="p">},</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The FairnessConfig helps in setting up and evaluating the fairness of the model predictions.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fairness_config</span> <span class="o">=</span> <span class="n">FairnessConfig</span><span class="p">(</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="n">fairness_metric_collection</span><span class="p">,</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># dataset is passed from the evaluator</span>
-    <span class="n">target_columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># target columns are passed from the evaluator</span>
-    <span class="n">groups</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;gender&quot;</span><span class="p">,</span> <span class="s2">&quot;age&quot;</span><span class="p">],</span>
-    <span class="n">group_bins</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">]},</span>
-    <span class="n">group_base_values</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="mi">40</span><span class="p">,</span> <span class="s2">&quot;gender&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">},</span>
-    <span class="n">thresholds</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="p">,</span> <span class="n">dataset_with_preds</span> <span class="o">=</span> <span class="n">mortality_task</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span>
-    <span class="n">metric_collection</span><span class="p">,</span>
-    <span class="n">model_names</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">prediction_column_prefix</span><span class="o">=</span><span class="s2">&quot;preds&quot;</span><span class="p">,</span>
-    <span class="n">slice_spec</span><span class="o">=</span><span class="n">slice_spec</span><span class="p">,</span>
-    <span class="n">batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">fairness_config</span><span class="o">=</span><span class="n">fairness_config</span><span class="p">,</span>
-    <span class="n">override_fairness_metrics</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Log the performance metrics to the report.</strong></p>
-<p>We can add a performance metric to the model card using the <code class="docutils literal notranslate"><span class="pre">log_performance_metric</span></code> method, which expects a dictionary where the keys are in the following format: <code class="docutils literal notranslate"><span class="pre">slice_name/metric_name</span></code>. For instance, <code class="docutils literal notranslate"><span class="pre">overall/accuracy</span></code>.</p>
-<p>We first need to process the evaluation results to get the metrics in the right format.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;model_for_preds.</span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="n">results_flat</span> <span class="o">=</span> <span class="n">flatten_results_dict</span><span class="p">(</span>
-    <span class="n">results</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
-    <span class="n">remove_metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># ruff: noqa: W505</span>
-<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">results_flat</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">split</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)</span>  <span class="c1"># noqa: PLW2901</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;BinaryPrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of predicted positive instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryRecall&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAccuracy&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of all instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAveragePrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryF1Score&quot;</span><span class="p">:</span> <span class="s2">&quot;The harmonic mean of precision and recall.&quot;</span><span class="p">,</span>
-    <span class="p">}</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">log_quantitative_analysis</span><span class="p">(</span>
-        <span class="s2">&quot;performance&quot;</span><span class="p">,</span>
-        <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
-        <span class="n">value</span><span class="o">=</span><span class="n">metric</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-        <span class="n">description</span><span class="o">=</span><span class="n">descriptions</span><span class="p">[</span><span class="n">name</span><span class="p">],</span>
-        <span class="n">metric_slice</span><span class="o">=</span><span class="n">split</span><span class="p">,</span>
-        <span class="n">pass_fail_thresholds</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
-        <span class="n">pass_fail_threshold_fns</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">),</span>
-    <span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>We can also use the <code class="docutils literal notranslate"><span class="pre">ClassificationPlotter</span></code> to plot the performance metrics and the add the figure to the model card using the <code class="docutils literal notranslate"><span class="pre">log_plotly_figure</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plotter</span> <span class="o">=</span> <span class="n">ClassificationPlotter</span><span class="p">(</span><span class="n">task_type</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">])</span>
-<span class="n">plotter</span><span class="o">.</span><span class="n">set_template</span><span class="p">(</span><span class="s2">&quot;plotly_white&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># extracting the ROC curves and AUROC results for all the slices</span>
-<span class="n">roc_curves</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">aurocs</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">roc_curves</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># extracting the precision-recall curves and average precision results for all the slices</span>
-<span class="n">pr_curves</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">average_precisions</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryAveragePrecision&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">pr_curves</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># plotting the ROC curves for all the slices</span>
-<span class="n">roc_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">roc_curve_comparison</span><span class="p">(</span><span class="n">roc_curves</span><span class="p">,</span> <span class="n">aurocs</span><span class="o">=</span><span class="n">aurocs</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">roc_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;ROC Curve Comparison&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">roc_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># plotting the precision-recall curves for all the slices</span>
-<span class="n">pr_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">precision_recall_curve_comparison</span><span class="p">(</span>
-    <span class="n">pr_curves</span><span class="p">,</span>
-    <span class="n">auprcs</span><span class="o">=</span><span class="n">average_precisions</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">pr_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Precision-Recall Curve Comparison&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">pr_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the overall classification metric values.</span>
-<span class="n">overall_performance</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-    <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;overall&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-    <span class="k">if</span> <span class="n">metric_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the overall classification metric values.</span>
-<span class="n">overall_performance_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_value</span><span class="p">(</span>
-    <span class="n">overall_performance</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Overall Performance&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">overall_performance_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Overall Performance&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">overall_performance_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the metric values for all the slices.</span>
-<span class="n">slice_metrics</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="p">{</span>
-        <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-        <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">slice_results</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">metric_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">]</span>
-    <span class="p">}</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the metric values for all the slices.</span>
-<span class="n">slice_metrics_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_comparison_bar</span><span class="p">(</span><span class="n">slice_metrics</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">slice_metrics_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Slice Metric Comparison&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">slice_metrics_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Reformatting the fairness metrics</span>
-<span class="n">fairness_results</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;fairness&quot;</span><span class="p">])</span>
-<span class="n">fairness_metrics</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="c1"># remove the group size from the fairness results and add it to the slice name</span>
-<span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">fairness_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">group_size</span> <span class="o">=</span> <span class="n">slice_results</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;Group Size&quot;</span><span class="p">)</span>
-    <span class="n">fairness_metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">slice_name</span><span class="si">}</span><span class="s2"> (Size=</span><span class="si">{</span><span class="n">group_size</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_results</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the fairness metrics</span>
-<span class="n">fairness_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_comparison_scatter</span><span class="p">(</span>
-    <span class="n">fairness_metrics</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Fairness Metrics&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fairness_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Fairness Metrics&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;fairness analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fairness_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Report-Generation">
-<h2>Report Generation<a class="headerlink" href="#Report-Generation" title="Link to this heading">#</a></h2>
-<p>Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.</p>
-<p>Let’s start with populating the model details section, which includes the following fields by default: - description: A high-level description of the model and its usage for a general audience. - version: The version of the model. - owners: The individuals or organizations that own the model. - license: The license under which the model is made available. - citation: The citation for the model. - references: Links to resources that are relevant to the model. - path: The path to where the model
-is stored. - regulatory_requirements: The regulatory requirements that are relevant to the model.</p>
-<p>We can add additional fields to the model details section by passing a dictionary to the <code class="docutils literal notranslate"><span class="pre">log_from_dict</span></code> method and specifying the section name as <code class="docutils literal notranslate"><span class="pre">model_details</span></code>. You can also use the <code class="docutils literal notranslate"><span class="pre">log_descriptor</span></code> method to add a new field object with a <code class="docutils literal notranslate"><span class="pre">description</span></code> attribute to any section of the model card.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;Mortality Prediction Model&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;The model was trained on the MIMICIV dataset </span><span class="se">\</span>
-<span class="s2">            to predict risk of in-hospital mortality.&quot;</span><span class="p">,</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;model_details&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_version</span><span class="p">(</span>
-    <span class="n">version_str</span><span class="o">=</span><span class="s2">&quot;0.0.1&quot;</span><span class="p">,</span>
-    <span class="n">date</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">date</span><span class="o">.</span><span class="n">today</span><span class="p">()),</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Initial Release&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_owner</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;CyclOps Team&quot;</span><span class="p">,</span>
-    <span class="n">contact</span><span class="o">=</span><span class="s2">&quot;vectorinstitute.github.io/cyclops/&quot;</span><span class="p">,</span>
-    <span class="n">email</span><span class="o">=</span><span class="s2">&quot;cyclops@vectorinstitute.ai&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_license</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">&quot;Apache-2.0&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_reference</span><span class="p">(</span>
-    <span class="n">link</span><span class="o">=</span><span class="s2">&quot;https://xgboost.readthedocs.io/en/stable/python/python_api.html&quot;</span><span class="p">,</span>  <span class="c1"># noqa: E501</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Next, let’s populate the considerations section, which includes the following fields by default: - users: The intended users of the model. - use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases. - fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms. - ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This
-can be populated using the <code class="docutils literal notranslate"><span class="pre">log_risk</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;users&quot;</span><span class="p">:</span> <span class="p">[</span>
-            <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;Hospitals&quot;</span><span class="p">},</span>
-            <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;Clinicians&quot;</span><span class="p">},</span>
-        <span class="p">],</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;considerations&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_user</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;ML Engineers&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_use_case</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Predicting prolonged length of stay&quot;</span><span class="p">,</span>
-    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;primary&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_fairness_assessment</span><span class="p">(</span>
-    <span class="n">affected_group</span><span class="o">=</span><span class="s2">&quot;sex, age&quot;</span><span class="p">,</span>
-    <span class="n">benefit</span><span class="o">=</span><span class="s2">&quot;Improved health outcomes for patients.&quot;</span><span class="p">,</span>
-    <span class="n">harm</span><span class="o">=</span><span class="s2">&quot;Biased predictions for patients in certain groups (e.g. older patients) </span><span class="se">\</span>
-<span class="s2">        may lead to worse health outcomes.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;We will monitor the performance of the model on these groups </span><span class="se">\</span>
-<span class="s2">        and retrain the model if the performance drops below a certain threshold.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_risk</span><span class="p">(</span>
-    <span class="n">risk</span><span class="o">=</span><span class="s2">&quot;The model may be used to make decisions that affect the health of patients.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;The model should be continuously monitored for performance </span><span class="se">\</span>
-<span class="s2">        and retrained if the performance drops below a certain threshold.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Once the model card is populated, you can generate the report using the <code class="docutils literal notranslate"><span class="pre">export</span></code> method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named <code class="docutils literal notranslate"><span class="pre">cyclops_reports</span></code> in the current working directory. You can change the path by passing a <code class="docutils literal notranslate"><span class="pre">output_dir</span></code> argument when instantiating the <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> class.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">synthetic_timestamps</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;2021-09-01&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;2021-10-01&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;2021-11-01&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;2021-12-01&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;2022-01-01&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-<span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">overview</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="n">report_path</span> <span class="o">=</span> <span class="n">report</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
-    <span class="n">output_filename</span><span class="o">=</span><span class="s2">&quot;mortality_report_periodic.html&quot;</span><span class="p">,</span>
-    <span class="n">synthetic_timestamp</span><span class="o">=</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-<span class="p">)</span>
-<span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">overview</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">quantitative_analysis</span><span class="o">.</span><span class="n">performance_metrics</span><span class="p">:</span>
-        <span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span>
-            <span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span>
-            <span class="mi">0</span><span class="p">,</span>
-            <span class="mi">1</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">metric</span><span class="o">.</span><span class="n">tests</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">passed</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">&gt;=</span> <span class="mf">0.7</span><span class="p">)</span>
-    <span class="n">report_path</span> <span class="o">=</span> <span class="n">report</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
-        <span class="n">output_filename</span><span class="o">=</span><span class="s2">&quot;mortality_report_periodic.html&quot;</span><span class="p">,</span>
-        <span class="n">synthetic_timestamp</span><span class="o">=</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span>
-    <span class="p">)</span>
-    <span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
-<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="s2">&quot;./cyclops_report&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>You can view the generated HTML <a class="reference external" href="./mortality_report_periodic.html">report</a>.</p>
-</section>
-</section>
-
-        </article>
-      </div>
-      <footer>
-        
-        <div class="related-pages">
-          <a class="next-page" href="../synthea/los_prediction.html">
-              <div class="page-info">
-                <div class="context">
-                  <span>Next</span>
-                </div>
-                <div class="title">Prolonged Length of Stay Prediction</div>
-              </div>
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-            </a>
-          <a class="prev-page" href="../kaggle/heart_failure_prediction.html">
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-              <div class="page-info">
-                <div class="context">
-                  <span>Previous</span>
-                </div>
-                
-                <div class="title">Heart Failure Prediction</div>
-                
-              </div>
-            </a>
-        </div>
-        <div class="bottom-of-page">
-          <div class="left-details">
-            <div class="copyright">
-                Copyright &#169; 2024, Vector AI Engineering
-            </div>
-            <!--
-            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-            
-            <a href="https://github.com/pradyunsg/furo">Furo</a>
-            
-            -->
-          </div>
-          <div class="right-details">
-            <div class="icons">
-              <a class="muted-link " href="https://github.com/VectorInstitute/cyclops" aria-label="GitHub">
-                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
-                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
-                </svg>
-            </a>
-              
-            </div>
-          </div>
-        </div>
-        <div class="footer-container" style="text-align: center;">
-          <div class="footer-small-text">
-              <br> All <a href="https://github.com/VectorInstitute/cyclops">source code for cyclops</a> is freely available under the terms of an <a href="https://opensource.org/licenses/Apache-2.0">Apache-2.0</a> license.
-          </div>
-
-          <div class="footer-small-text">
-            This work is made possible due to the data obtained from the <a href="https://www.geminimedicine.ca/">GEMINI study</a>.
-          </div>
-
-          <div class="logo">
-            <a class="logo" href="https://vectorinstitute.ai/">
-              <span class="logo"></span>
-              <img alt="Logo of the Vector Institute" style="width:90px; max-width:90px;" src="../../_static/logos/vector_logo.png" />
-            </a>
-            <a class="logo" href="https://www.geminimedicine.ca/">
-              <span class="logo"></span>
-              <img alt="Logo of GEMINI" style="width:90px; max-width:90px;" src="../../_static/logos/gemini_logo.png" />
-            </a>
-          </div>
-        </div>
-        
-      </footer>
-    </div>
-    <aside class="toc-drawer">
-      
-      
-      <div class="toc-sticky toc-scroll">
-        <div class="toc-title-container">
-          <span class="toc-title">
-            On this page
-          </span>
-        </div>
-        <div class="toc-tree-container">
-          <div class="toc-tree">
-            <ul>
-<li><a class="reference internal" href="#">Mortality Prediction</a><ul>
-<li><a class="reference internal" href="#Import-Libraries">Import Libraries</a></li>
-<li><a class="reference internal" href="#Constants">Constants</a></li>
-<li><a class="reference internal" href="#Data-Querying-&amp;-Processing">Data Querying &amp; Processing</a><ul>
-<li><a class="reference internal" href="#Compute-mortality-(labels)">Compute mortality (labels)</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#Data-Inspection-and-Preprocessing">Data Inspection and Preprocessing</a><ul>
-<li><a class="reference internal" href="#Drop-NaNs-based-on-the-NAN_THRESHOLD">Drop NaNs based on the <code class="docutils literal notranslate"><span class="pre">NAN_THRESHOLD</span></code></a></li>
-<li><a class="reference internal" href="#Outcome-distribution">Outcome distribution</a></li>
-<li><a class="reference internal" href="#Gender-distribution">Gender distribution</a></li>
-<li><a class="reference internal" href="#Age-distribution">Age distribution</a></li>
-<li><a class="reference internal" href="#Identifying-feature-types">Identifying feature types</a></li>
-<li><a class="reference internal" href="#Creating-data-preprocessors">Creating data preprocessors</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#Creating-Hugging-Face-Dataset">Creating Hugging Face Dataset</a></li>
-<li><a class="reference internal" href="#Model-Creation">Model Creation</a></li>
-<li><a class="reference internal" href="#Task-Creation">Task Creation</a></li>
-<li><a class="reference internal" href="#Training">Training</a></li>
-<li><a class="reference internal" href="#Prediction">Prediction</a></li>
-<li><a class="reference internal" href="#Evaluation">Evaluation</a></li>
-<li><a class="reference internal" href="#Report-Generation">Report Generation</a></li>
-</ul>
-</li>
-</ul>
-
-          </div>
-        </div>
-      </div>
-      
-      
-    </aside>
-  </div>
-</div></body>
-</html>
\ No newline at end of file
diff --git a/api/tutorials/mimiciv/mortality_prediction.ipynb b/api/tutorials/mimiciv/mortality_prediction.ipynb
deleted file mode 100644
index 5ad66b0c6..000000000
--- a/api/tutorials/mimiciv/mortality_prediction.ipynb
+++ /dev/null
@@ -1,1325 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Mortality Prediction\n",
-    "\n",
-    "This notebook showcases mortality prediction on the [MIMICIV](https://physionet.org/content/mimiciv/2.0) dataset using CyclOps. The task is formulated as a binary classification task, whether the patient will die within the next N days. The prediction can be made after M number of days after admission. For example, if N = 14 and M = 1, we are predicting risk of patient mortality within 14 days of admission after considering 24 hours of data after admission."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"Mortality Prediction.\"\"\"\n",
-    "\n",
-    "import copy\n",
-    "import shutil\n",
-    "from datetime import date\n",
-    "\n",
-    "import cycquery.ops as qo\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "import plotly.graph_objects as go\n",
-    "from cycquery import MIMICIVQuerier\n",
-    "from datasets import Dataset\n",
-    "from datasets.features import ClassLabel\n",
-    "from imblearn.over_sampling import SMOTE\n",
-    "from imblearn.pipeline import Pipeline as ImbPipeline\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
-    "\n",
-    "from cyclops.data.aggregate import RESTRICT_TIMESTAMP, Aggregator\n",
-    "from cyclops.data.clean import normalize_names\n",
-    "from cyclops.data.df.feature import TabularFeatures\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate.fairness import FairnessConfig  # noqa: E402\n",
-    "from cyclops.evaluate.metrics import create_metric\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.models.catalog import create_model\n",
-    "from cyclops.report import ModelCardReport\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter\n",
-    "from cyclops.report.utils import flatten_results_dict\n",
-    "from cyclops.tasks import BinaryTabularClassificationTask\n",
-    "from cyclops.utils.common import add_years_approximate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "CyclOps offers a package for documentation of the model through a model report. The `ModelCardReport` class is used to populate and generate the model report as an HTML file. The model report has the following sections:\n",
-    "\n",
-    "- Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).\n",
-    "- Datasets: High level statistics of the training data, including changes in distribution over time.\n",
-    "- Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.\n",
-    "- Fairness Analysis: This section contains the fairness metrics of the model.\n",
-    "- Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.\n",
-    "- Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.\n",
-    "- Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.\n",
-    "\n",
-    "We will use this to document the model development process as we go along and generate the model report at the end.\n",
-    "\n",
-    "`The model report tool is a work in progress and is subject to change.`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "M = 1\n",
-    "N = 14\n",
-    "NAN_THRESHOLD = 0.25\n",
-    "TRAIN_SIZE = 0.8\n",
-    "RANDOM_SEED = 12"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Querying & Processing\n",
-    "\n",
-    "### Compute mortality (labels)\n",
-    "\n",
-    "1. Get encounters\n",
-    "2. Filter out encounters less than M days\n",
-    "3. Set label = 1 for encounters where deathtime is within N days after admission\n",
-    "4. Get lab events\n",
-    "5. Aggregate them by computing mean, merge with encounter data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "querier = MIMICIVQuerier(\n",
-    "    dbms=\"postgresql\",\n",
-    "    port=5432,\n",
-    "    host=\"localhost\",\n",
-    "    database=\"mimiciv-2.0\",\n",
-    "    user=\"postgres\",\n",
-    "    password=\"pwd\",\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def get_encounters():\n",
-    "    \"\"\"Get encounters data.\"\"\"\n",
-    "    patients = querier.patients()\n",
-    "    encounters = querier.mimiciv_hosp.admissions()\n",
-    "    drop_op = qo.Drop(\n",
-    "        [\"language\", \"marital_status\", \"edregtime\", \"edouttime\"],\n",
-    "    )\n",
-    "    encounters = encounters.ops(drop_op)\n",
-    "    patient_encounters = patients.join(encounters, on=\"subject_id\")\n",
-    "    patient_encounters = patient_encounters.run()\n",
-    "    patient_encounters[\"age\"] = (\n",
-    "        patient_encounters[\"admittime\"].dt.year\n",
-    "        - patient_encounters[\"anchor_year\"]\n",
-    "        + patient_encounters[\"anchor_age\"]\n",
-    "    )\n",
-    "    for col in [\"admittime\", \"dischtime\", \"deathtime\"]:\n",
-    "        patient_encounters[col] = add_years_approximate(\n",
-    "            patient_encounters[col],\n",
-    "            patient_encounters[\"anchor_year_difference\"],\n",
-    "        )\n",
-    "\n",
-    "    return patient_encounters[\n",
-    "        [\n",
-    "            \"hadm_id\",\n",
-    "            \"admittime\",\n",
-    "            \"dischtime\",\n",
-    "            \"deathtime\",\n",
-    "            \"anchor_age\",\n",
-    "            \"age\",\n",
-    "            \"gender\",\n",
-    "            \"anchor_year_difference\",\n",
-    "            \"admission_location\",\n",
-    "            \"admission_type\",\n",
-    "            \"insurance\",\n",
-    "            \"hospital_expire_flag\",\n",
-    "        ]\n",
-    "    ]\n",
-    "\n",
-    "\n",
-    "def compute_mortality_outcome(patient_encounters):\n",
-    "    \"\"\"Compute mortality outcome.\"\"\"\n",
-    "    # Drop encounters ending in death which don't have a death timestamp\n",
-    "    invalid = (patient_encounters[\"hospital_expire_flag\"] == 1) & (\n",
-    "        patient_encounters[\"deathtime\"].isna()\n",
-    "    )\n",
-    "    patient_encounters = patient_encounters[~invalid]\n",
-    "    print(f\"Encounters with death flag but no death timestamp: {invalid.sum()}\")\n",
-    "    # Drop encounters which are shorter than M days\n",
-    "    invalid = (\n",
-    "        patient_encounters[\"dischtime\"] - patient_encounters[\"admittime\"]\n",
-    "    ).dt.days < M\n",
-    "    patient_encounters = patient_encounters[~invalid]\n",
-    "    print(f\"Encounters shorter than {M} days: {invalid.sum()}\")\n",
-    "    # Death timestamp is within (<=) N days of admission\n",
-    "    valid = (\n",
-    "        patient_encounters[\"deathtime\"] - patient_encounters[\"admittime\"]\n",
-    "    ).dt.days <= N\n",
-    "    print(f\"Encounters with death timestamp within {N} days: {valid.sum()}\")\n",
-    "    # (Died in hospital) & (Death timestamp is defined)\n",
-    "    print(len(patient_encounters))\n",
-    "    patient_encounters[\"mortality_outcome\"] = pd.Series(\n",
-    "        [0] * len(patient_encounters),\n",
-    "        index=patient_encounters.index,\n",
-    "        dtype=\"int64[pyarrow]\",\n",
-    "    )\n",
-    "    patient_encounters.loc[valid, \"mortality_outcome\"] = 1\n",
-    "    print(\n",
-    "        f\"Encounters with mortality outcome for the model: {patient_encounters['mortality_outcome'].sum()}\",\n",
-    "    )\n",
-    "\n",
-    "    return patient_encounters\n",
-    "\n",
-    "\n",
-    "def get_labevents(patient_encounters):\n",
-    "    \"\"\"Get labevents data.\"\"\"\n",
-    "    labevents = querier.labevents().run(index_col=\"hadm_id\", batch_mode=True)\n",
-    "\n",
-    "    def process_labevents(labevents, patient_encounters):\n",
-    "        \"\"\"Process labevents before aggregation.\"\"\"\n",
-    "        # Reverse deidentified dating\n",
-    "        labevents = pd.merge(\n",
-    "            patient_encounters[\n",
-    "                [\n",
-    "                    \"hadm_id\",\n",
-    "                    \"anchor_year_difference\",\n",
-    "                ]\n",
-    "            ],\n",
-    "            labevents,\n",
-    "            on=\"hadm_id\",\n",
-    "        )\n",
-    "        labevents[\"charttime\"] = add_years_approximate(\n",
-    "            labevents[\"charttime\"],\n",
-    "            labevents[\"anchor_year_difference\"],\n",
-    "        )\n",
-    "        labevents = labevents.drop(\"anchor_year_difference\", axis=1)\n",
-    "        # Pre-processing\n",
-    "        labevents[\"label\"] = normalize_names(labevents[\"label\"])\n",
-    "        labevents[\"category\"] = normalize_names(labevents[\"category\"])\n",
-    "\n",
-    "        return labevents\n",
-    "\n",
-    "    start_timestamps = (\n",
-    "        patient_encounters[[\"hadm_id\", \"admittime\"]]\n",
-    "        .set_index(\"hadm_id\")\n",
-    "        .rename({\"admittime\": RESTRICT_TIMESTAMP}, axis=1)\n",
-    "    )\n",
-    "    mean_aggregator = Aggregator(\n",
-    "        aggfuncs={\n",
-    "            \"valuenum\": \"mean\",\n",
-    "        },\n",
-    "        window_duration=M * 24,\n",
-    "        window_start_time=start_timestamps,\n",
-    "        timestamp_col=\"charttime\",\n",
-    "        time_by=\"hadm_id\",\n",
-    "        agg_by=[\"hadm_id\", \"label\"],\n",
-    "    )\n",
-    "    means_df = pd.DataFrame()\n",
-    "    for batch_num, labevents_batch in enumerate(labevents):\n",
-    "        labevents_batch = process_labevents(  # noqa: PLW2901\n",
-    "            labevents_batch,\n",
-    "            patient_encounters,\n",
-    "        )\n",
-    "        means = mean_aggregator.fit_transform(\n",
-    "            labevents_batch,\n",
-    "        )\n",
-    "        means = means.reset_index()\n",
-    "        means = means.pivot(index=\"hadm_id\", columns=\"label\", values=\"valuenum\")\n",
-    "        means = means.add_prefix(\"lab_\")\n",
-    "        means = pd.merge(\n",
-    "            patient_encounters[\n",
-    "                [\n",
-    "                    \"hadm_id\",\n",
-    "                    \"mortality_outcome\",\n",
-    "                    \"age\",\n",
-    "                    \"gender\",\n",
-    "                    \"admission_location\",\n",
-    "                ]\n",
-    "            ],\n",
-    "            means,\n",
-    "            on=\"hadm_id\",\n",
-    "        )\n",
-    "        means_df = pd.concat([means_df, means])\n",
-    "        if batch_num == 2:\n",
-    "            break\n",
-    "        print(\"Processing batch {}\".format(batch_num + 1))\n",
-    "\n",
-    "    return means_df\n",
-    "\n",
-    "\n",
-    "def run_query():\n",
-    "    \"\"\"Run query.\"\"\"\n",
-    "    cohort = get_encounters()\n",
-    "    cohort = compute_mortality_outcome(cohort)\n",
-    "\n",
-    "    return get_labevents(cohort)\n",
-    "\n",
-    "\n",
-    "cohort = run_query()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Inspection and Preprocessing\n",
-    "\n",
-    "### Drop NaNs based on the `NAN_THRESHOLD`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "null_counts = cohort.isnull().sum()[cohort.isnull().sum() > 0]\n",
-    "fig = go.Figure(data=[go.Bar(x=null_counts.index, y=null_counts.values)])\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    title=\"Number of Null Values per Column\",\n",
-    "    xaxis_title=\"Columns\",\n",
-    "    yaxis_title=\"Number of Null Values\",\n",
-    "    height=600,\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**\n",
-    "\n",
-    "We can use the log_plotly_figure method to add the figure to a section of the report. One can specify whether the figure should be interactive or not by setting the `interactive` parameter to `True` or `False` respectively. The default value is `True`. This\n",
-    "also affects the final size of the report. If the figure is interactive, the size of the report will be larger than if the figure is not interactive. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Number of Null Values per Column\",\n",
-    "    section_name=\"datasets\",\n",
-    "    interactive=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "thresh_nan = int(NAN_THRESHOLD * len(cohort))\n",
-    "cohort = cohort.dropna(axis=1, thresh=thresh_nan)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Outcome distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.pie(cohort, names=\"mortality_outcome\")\n",
-    "fig.update_traces(textinfo=\"percent+label\")\n",
-    "fig.update_layout(title_text=\"Outcome Distribution\")\n",
-    "fig.update_traces(\n",
-    "    hovertemplate=\"Outcome: %{label}<br>Count: \\\n",
-    "    %{value}<br>Percent: %{percent}\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Outcome Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The data is heavily unbalanced.\n",
-    "class_counts = cohort[\"mortality_outcome\"].value_counts()\n",
-    "class_ratio = class_counts[0] / class_counts[1]\n",
-    "print(class_ratio, class_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Gender distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.pie(cohort, names=\"gender\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Gender Distribution\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Gender Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###  Age distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(cohort, x=\"age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Age Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Identifying feature types\n",
-    "\n",
-    "Cyclops `TabularFeatures` class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features_list = set(cohort.columns.tolist()) - {\"hadm_id\", \"mortality_outcome\"}\n",
-    "features_list = sorted(features_list)\n",
-    "tab_features = TabularFeatures(\n",
-    "    data=cohort.reset_index(),\n",
-    "    features=features_list,\n",
-    "    by=\"hadm_id\",\n",
-    "    targets=\"mortality_outcome\",\n",
-    ")\n",
-    "print(tab_features.types)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating data preprocessors\n",
-    "\n",
-    "We create a data preprocessor using sklearn's ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numeric_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"mean\")), (\"scaler\", MinMaxScaler())],\n",
-    ")\n",
-    "binary_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"most_frequent\"))],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numeric_features = sorted((tab_features.features_by_type(\"numeric\")))\n",
-    "numeric_indices = [\n",
-    "    cohort[features_list].columns.get_loc(column) for column in numeric_features\n",
-    "]\n",
-    "print(numeric_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "binary_features = sorted(tab_features.features_by_type(\"binary\"))\n",
-    "ordinal_features = sorted(tab_features.features_by_type(\"ordinal\"))\n",
-    "binary_features.remove(\"mortality_outcome\")\n",
-    "binary_indices = [\n",
-    "    cohort[features_list].columns.get_loc(column) for column in binary_features\n",
-    "]\n",
-    "ordinal_indices = [\n",
-    "    cohort[features_list].columns.get_loc(column) for column in ordinal_features\n",
-    "]\n",
-    "print(binary_features, ordinal_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preprocessor = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        (\"num\", numeric_transformer, numeric_indices),\n",
-    "        (\n",
-    "            \"onehot\",\n",
-    "            OneHotEncoder(handle_unknown=\"ignore\"),\n",
-    "            binary_indices + ordinal_indices,\n",
-    "        ),\n",
-    "    ],\n",
-    "    remainder=\"passthrough\",\n",
-    ")\n",
-    "preprocessor_pipeline = [\n",
-    "    (\"preprocessor\", preprocessor),\n",
-    "    (\"oversampling\", SMOTE(random_state=RANDOM_SEED)),\n",
-    "]\n",
-    "preprocessor_pipeline = ImbPipeline(preprocessor_pipeline)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating Hugging Face Dataset\n",
-    "\n",
-    "We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cohort = cohort.drop(columns=[\"hadm_id\"])\n",
-    "dataset = Dataset.from_pandas(cohort)\n",
-    "dataset.cleanup_cache_files()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset = dataset.cast_column(\"mortality_outcome\", ClassLabel(num_classes=2))\n",
-    "dataset = dataset.train_test_split(\n",
-    "    train_size=TRAIN_SIZE,\n",
-    "    stratify_by_column=\"mortality_outcome\",\n",
-    "    seed=RANDOM_SEED,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model Creation\n",
-    "\n",
-    "CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a XGBoost classifier to fit a logisitic regression model. The model configurations can be passed to `create_model` based on the parameters for XGBClassifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_name = \"xgb_classifier\"\n",
-    "model = create_model(model_name, random_state=123)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Task Creation\n",
-    "\n",
-    "We use Cyclops tasks to define our model's task (in this case, BinaryTabularClassificationTask), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mortality_task = BinaryTabularClassificationTask(\n",
-    "    {model_name: model},\n",
-    "    task_features=features_list,\n",
-    "    task_target=\"mortality_outcome\",\n",
-    ")\n",
-    "mortality_task.list_models()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training\n",
-    "\n",
-    "If `best_model_params` is passed to the `train` method, the best model will be selected after the hyperparameter search. The parameters in `best_model_params` indicate the values to create the parameters grid.\n",
-    "\n",
-    "Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_model_params = {\n",
-    "    \"n_estimators\": [100, 250, 500],\n",
-    "    \"learning_rate\": [0.1, 0.01],\n",
-    "    \"max_depth\": [2, 5],\n",
-    "    \"reg_lambda\": [0, 1, 10],\n",
-    "    \"colsample_bytree\": [0.7, 0.8, 1],\n",
-    "    \"gamma\": [0, 1, 2, 10],\n",
-    "    \"method\": \"random\",\n",
-    "}\n",
-    "mortality_task.train(\n",
-    "    dataset[\"train\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor_pipeline,\n",
-    "    best_model_params=best_model_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_params = mortality_task.list_models_params()[model_name]\n",
-    "print(model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the model parameters to the report.**\n",
-    "\n",
-    "We can add model parameters to the model card using the `log_model_parameters` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_model_parameters(params=model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prediction\n",
-    "\n",
-    "The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = mortality_task.predict(\n",
-    "    dataset[\"test\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    proba=False,\n",
-    "    only_predictions=True,\n",
-    ")\n",
-    "print(len(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluation\n",
-    "\n",
-    "Evaluation is done using various evaluation metrics that provide different perspectives on the model's predictive abilities i.e. standard performance metrics and fairness metrics.\n",
-    "\n",
-    "The standard performance metrics can be created using the `MetricDict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_auroc\",\n",
-    "    \"binary_average_precision\",\n",
-    "    \"binary_roc_curve\",\n",
-    "    \"binary_precision_recall_curve\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using `SliceSpec` objects. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"min_value\": 20,\n",
-    "            \"max_value\": 50,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"min_value\": 50,\n",
-    "            \"max_value\": 80,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\"gender\": {\"value\": \"M\"}},\n",
-    "    {\"gender\": {\"value\": \"F\"}},\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `MetricDict` can also be defined for the fairness metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "specificity = create_metric(metric_name=\"binary_specificity\", experimental=True)\n",
-    "sensitivity = create_metric(metric_name=\"binary_sensitivity\", experimental=True)\n",
-    "fpr = -specificity + 1  # __rsub__ is not implemented for metrics\n",
-    "fnr = -sensitivity + 1\n",
-    "ber = (fpr + fnr) / 2\n",
-    "fairness_metric_collection = MetricDict(\n",
-    "    {\n",
-    "        \"Sensitivity\": sensitivity,\n",
-    "        \"Specificity\": specificity,\n",
-    "        \"BER\": ber,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The FairnessConfig helps in setting up and evaluating the fairness of the model predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fairness_config = FairnessConfig(\n",
-    "    metrics=fairness_metric_collection,\n",
-    "    dataset=None,  # dataset is passed from the evaluator\n",
-    "    target_columns=None,  # target columns are passed from the evaluator\n",
-    "    groups=[\"gender\", \"age\"],\n",
-    "    group_bins={\"age\": [20, 40]},\n",
-    "    group_base_values={\"age\": 40, \"gender\": \"M\"},\n",
-    "    thresholds=[0.5],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, dataset_with_preds = mortality_task.evaluate(\n",
-    "    dataset[\"test\"],\n",
-    "    metric_collection,\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=slice_spec,\n",
-    "    batch_size=-1,\n",
-    "    fairness_config=fairness_config,\n",
-    "    override_fairness_metrics=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Log the performance metrics to the report.**\n",
-    "\n",
-    "We can add a performance metric to the model card using the `log_performance_metric` method, which expects a dictionary where the keys are in the following format: `slice_name/metric_name`. For instance, `overall/accuracy`. \n",
-    "\n",
-    "We first need to process the evaluation results to get the metrics in the right format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_name = f\"model_for_preds.{model_name}\"\n",
-    "results_flat = flatten_results_dict(\n",
-    "    results=results,\n",
-    "    remove_metrics=[\"BinaryROC\", \"BinaryPrecisionRecallCurve\"],\n",
-    "    model_name=model_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ruff: noqa: W505\n",
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryAveragePrecision\": \"The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the `ClassificationPlotter` to plot the performance metrics and the add the figure to the model card using the `log_plotly_figure` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the ROC curves and AUROC results for all the slices\n",
-    "roc_curves = {\n",
-    "    slice_name: slice_results[\"BinaryROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "aurocs = {\n",
-    "    slice_name: slice_results[\"BinaryAUROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "roc_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# extracting the precision-recall curves and average precision results for all the slices\n",
-    "pr_curves = {\n",
-    "    slice_name: slice_results[\"BinaryPrecisionRecallCurve\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "average_precisions = {\n",
-    "    slice_name: slice_results[\"BinaryAveragePrecision\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "pr_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the ROC curves for all the slices\n",
-    "roc_plot = plotter.roc_curve_comparison(roc_curves, aurocs=aurocs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=roc_plot,\n",
-    "    caption=\"ROC Curve Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "roc_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plotting the precision-recall curves for all the slices\n",
-    "pr_plot = plotter.precision_recall_curve_comparison(\n",
-    "    pr_curves,\n",
-    "    auprcs=average_precisions,\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=pr_plot,\n",
-    "    caption=\"Precision-Recall Curve Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "pr_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the overall classification metric values.\n",
-    "overall_performance = {\n",
-    "    metric_name: metric_value\n",
-    "    for metric_name, metric_value in results[model_name][\"overall\"].items()\n",
-    "    if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the overall classification metric values.\n",
-    "overall_performance_plot = plotter.metrics_value(\n",
-    "    overall_performance,\n",
-    "    title=\"Overall Performance\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=overall_performance_plot,\n",
-    "    caption=\"Overall Performance\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "overall_performance_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=slice_metrics_plot,\n",
-    "    caption=\"Slice Metric Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reformatting the fairness metrics\n",
-    "fairness_results = copy.deepcopy(results[\"fairness\"])\n",
-    "fairness_metrics = {}\n",
-    "# remove the group size from the fairness results and add it to the slice name\n",
-    "for slice_name, slice_results in fairness_results.items():\n",
-    "    group_size = slice_results.pop(\"Group Size\")\n",
-    "    fairness_metrics[f\"{slice_name} (Size={group_size})\"] = slice_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the fairness metrics\n",
-    "fairness_plot = plotter.metrics_comparison_scatter(\n",
-    "    fairness_metrics,\n",
-    "    title=\"Fairness Metrics\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fairness_plot,\n",
-    "    caption=\"Fairness Metrics\",\n",
-    "    section_name=\"fairness analysis\",\n",
-    ")\n",
-    "fairness_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Report Generation\n",
-    "\n",
-    "Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.\n",
-    "\n",
-    "\n",
-    "Let's start with populating the model details section, which includes the following fields by default:\n",
-    "- description: A high-level description of the model and its usage for a general audience.\n",
-    "- version: The version of the model.\n",
-    "- owners: The individuals or organizations that own the model.\n",
-    "- license: The license under which the model is made available.\n",
-    "- citation: The citation for the model.\n",
-    "- references: Links to resources that are relevant to the model.\n",
-    "- path: The path to where the model is stored.\n",
-    "- regulatory_requirements: The regulatory requirements that are relevant to the model.\n",
-    "\n",
-    "We can add additional fields to the model details section by passing a dictionary to the `log_from_dict` method and specifying the section name as `model_details`. You can also use the `log_descriptor` method to add a new field object with a `description` attribute to any section of the model card."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"Mortality Prediction Model\",\n",
-    "        \"description\": \"The model was trained on the MIMICIV dataset \\\n",
-    "            to predict risk of in-hospital mortality.\",\n",
-    "    },\n",
-    "    section_name=\"model_details\",\n",
-    ")\n",
-    "report.log_version(\n",
-    "    version_str=\"0.0.1\",\n",
-    "    date=str(date.today()),\n",
-    "    description=\"Initial Release\",\n",
-    ")\n",
-    "report.log_owner(\n",
-    "    name=\"CyclOps Team\",\n",
-    "    contact=\"vectorinstitute.github.io/cyclops/\",\n",
-    "    email=\"cyclops@vectorinstitute.ai\",\n",
-    ")\n",
-    "report.log_license(identifier=\"Apache-2.0\")\n",
-    "report.log_reference(\n",
-    "    link=\"https://xgboost.readthedocs.io/en/stable/python/python_api.html\",  # noqa: E501\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, let's populate the considerations section, which includes the following fields by default:\n",
-    "- users: The intended users of the model.\n",
-    "- use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases.\n",
-    "- fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms.\n",
-    "- ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This can be populated using the  `log_risk` method.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"users\": [\n",
-    "            {\"description\": \"Hospitals\"},\n",
-    "            {\"description\": \"Clinicians\"},\n",
-    "        ],\n",
-    "    },\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_user(description=\"ML Engineers\")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting prolonged length of stay\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"sex, age\",\n",
-    "    benefit=\"Improved health outcomes for patients.\",\n",
-    "    harm=\"Biased predictions for patients in certain groups (e.g. older patients) \\\n",
-    "        may lead to worse health outcomes.\",\n",
-    "    mitigation_strategy=\"We will monitor the performance of the model on these groups \\\n",
-    "        and retrain the model if the performance drops below a certain threshold.\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"The model may be used to make decisions that affect the health of patients.\",\n",
-    "    mitigation_strategy=\"The model should be continuously monitored for performance \\\n",
-    "        and retrained if the performance drops below a certain threshold.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the model card is populated, you can generate the report using the `export` method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named `cyclops_reports` in the current working directory. You can change the path by passing a `output_dir` argument when instantiating the `ModelCardReport` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "synthetic_timestamps = [\n",
-    "    \"2021-09-01\",\n",
-    "    \"2021-10-01\",\n",
-    "    \"2021-11-01\",\n",
-    "    \"2021-12-01\",\n",
-    "    \"2022-01-01\",\n",
-    "]\n",
-    "report._model_card.overview = None\n",
-    "report_path = report.export(\n",
-    "    output_filename=\"mortality_report_periodic.html\",\n",
-    "    synthetic_timestamp=synthetic_timestamps[0],\n",
-    ")\n",
-    "shutil.copy(f\"{report_path}\", \".\")\n",
-    "for i in range(4):\n",
-    "    report._model_card.overview = None\n",
-    "    for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "        metric.value = np.clip(\n",
-    "            metric.value + np.random.normal(0, 0.1),\n",
-    "            0,\n",
-    "            1,\n",
-    "        )\n",
-    "        metric.tests[0].passed = bool(metric.value >= 0.7)\n",
-    "    report_path = report.export(\n",
-    "        output_filename=\"mortality_report_periodic.html\",\n",
-    "        synthetic_timestamp=synthetic_timestamps[i + 1],\n",
-    "    )\n",
-    "    shutil.copy(f\"{report_path}\", \".\")\n",
-    "shutil.rmtree(\"./cyclops_report\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can view the generated HTML [report](./mortality_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/api/tutorials/nihcxr/cxr_classification.html b/api/tutorials/nihcxr/cxr_classification.html
deleted file mode 100644
index 4e8c6ea61..000000000
--- a/api/tutorials/nihcxr/cxr_classification.html
+++ /dev/null
@@ -1,923 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en">
-  <head><meta charset="utf-8"/>
-    <meta name="viewport" content="width=device-width,initial-scale=1"/>
-    <meta name="color-scheme" content="light dark">
-    <meta name="google-site-verification" content="i0qQRaR9OA3tSz_9tDocdcXGY27Ox_cy4FrvTHD2C_0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-
-    
-      <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="🧑🏿‍💻 Developing" href="../../developing.html" /><link rel="prev" title="Readmission Prediction" href="../diabetes_130/readmission_prediction.html" />
-
-    <link rel="shortcut icon" href="../../_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
-
-    
-    <meta name="msvalidate.01" content="8D2A5032F006424CAF54C0DDCD16F666" />
-      
-        <title>Chest X-Ray Disease Classification - cyclops documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
-    <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="../../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
-    <link rel="stylesheet" type="text/css" href="../../_static/nbsphinx-code-cells.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/css/cyclops.css?v=9f20b50b" />
-    
-    
-
-
-<style>
-  body {
-    --color-code-background: #f8f8f8;
-  --color-code-foreground: black;
-  --color-card-background: #5e88f2;
-  
-  }
-  @media not print {
-    body[data-theme="dark"] {
-      --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-    }
-    @media (prefers-color-scheme: dark) {
-      body:not([data-theme="light"]) {
-        --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-      }
-    }
-  }
-</style><script src="../../_static/documentation_options.js?v=5929fcd5"></script>
-    <script src="../../_static/doctools.js?v=888ff710"></script>
-    <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="../../_static/scripts/furo.js?v=32e29ea5"></script>
-    <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
-    <script src="../../_static/copybutton.js?v=ccdb6887"></script>
-    <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-    <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script src="../../_static/require.min.js?v=53367eec"></script>
-    <script src="../../_static/custom.js?v=275847af"></script>
-    
-    <script src="https://kit.fontawesome.com/2c1f516901.js" crossorigin="anonymous"></script>
-
-  </head>
-  <body>
-    
-    <script>
-      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
-    </script>
-    
-
-<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
-  <symbol id="svg-toc" viewBox="0 0 24 24">
-    <title>Contents</title>
-    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
-      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-menu" viewBox="0 0 24 24">
-    <title>Menu</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
-      <line x1="3" y1="12" x2="21" y2="12"></line>
-      <line x1="3" y1="6" x2="21" y2="6"></line>
-      <line x1="3" y1="18" x2="21" y2="18"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
-    <title>Expand</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
-      <polyline points="9 18 15 12 9 6"></polyline>
-    </svg>
-  </symbol>
-  <symbol id="svg-sun" viewBox="0 0 24 24">
-    <title>Light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
-      <circle cx="12" cy="12" r="5"></circle>
-      <line x1="12" y1="1" x2="12" y2="3"></line>
-      <line x1="12" y1="21" x2="12" y2="23"></line>
-      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
-      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
-      <line x1="1" y1="12" x2="3" y2="12"></line>
-      <line x1="21" y1="12" x2="23" y2="12"></line>
-      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
-      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon" viewBox="0 0 24 24">
-    <title>Dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
-      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
-    </svg>
-  </symbol>
-  <symbol id="svg-sun-half" viewBox="0 0 24 24">
-    <title>Auto light/dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-      <circle cx="12" cy="12" r="9" />
-      <path d="M13 12h5" />
-      <path d="M13 15h4" />
-      <path d="M13 18h1" />
-      <path d="M13 9h4" />
-      <path d="M13 6h1" />
-    </svg>
-  </symbol>
-</svg>
-
-<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
-<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
-<label class="overlay sidebar-overlay" for="__navigation">
-  <div class="visually-hidden">Hide navigation sidebar</div>
-</label>
-<label class="overlay toc-overlay" for="__toc">
-  <div class="visually-hidden">Hide table of contents sidebar</div>
-</label>
-
-
-
-<div class="page">
-  <header class="mobile-header">
-    <div class="header-left">
-      <label class="nav-overlay-icon" for="__navigation">
-        <div class="visually-hidden">Toggle site navigation sidebar</div>
-        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
-      </label>
-    </div>
-    <div class="header-center">
-      <a href="../../index.html"><div class="brand">cyclops  documentation</div></a>
-    </div>
-    <div class="header-right">
-      <div class="theme-toggle-container theme-toggle-header">
-        <button class="theme-toggle">
-          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-        </button>
-      </div>
-      <label class="toc-overlay-icon toc-header-icon" for="__toc">
-        <div class="visually-hidden">Toggle table of contents sidebar</div>
-        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-      </label>
-    </div>
-  </header>
-  <aside class="sidebar-drawer">
-    <div class="sidebar-container">
-      
-      <div class="sidebar-sticky"><a class="sidebar-brand" href="../../index.html">
-  
-  <div class="sidebar-logo-container">
-    <img class="sidebar-logo" src="../../_static/cyclops_logo-dark.png" alt="Logo"/>
-  </div>
-  
-  
-</a><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
-  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
-  <input type="hidden" name="check_keywords" value="yes">
-  <input type="hidden" name="area" value="default">
-</form>
-<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <ul class="current">
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l2 current has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="../kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">Chest X-Ray Disease Classification</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../developing.html">🧑🏿‍💻 Developing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing to cyclops</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../api.html">API Reference</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of API Reference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.data.html">cyclops.data</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of cyclops.data</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.html">cyclops.data.slicer</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of cyclops.data.slicer</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.compound_filter.html">cyclops.data.slicer.compound_filter</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html">cyclops.data.slicer.filter_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html">cyclops.data.slicer.filter_non_null</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_range.html">cyclops.data.slicer.filter_range</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html">cyclops.data.slicer.filter_string_contains</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_value.html">cyclops.data.slicer.filter_value</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.is_datetime.html">cyclops.data.slicer.is_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.overall.html">cyclops.data.slicer.overall</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html">cyclops.data.slicer.SliceSpec</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.html">cyclops.data.aggregate</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of cyclops.data.aggregate</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html">cyclops.data.aggregate.tabular_as_aggregated</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html">cyclops.data.aggregate.timestamp_ffill_agg</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html">cyclops.data.aggregate.Aggregator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.html">cyclops.data.features.medical_image</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of cyclops.data.features.medical_image</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html">cyclops.data.features.medical_image.MedicalImage</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.tasks.html">cyclops.tasks</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of cyclops.tasks</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.html">cyclops.tasks.classification</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of cyclops.tasks.classification</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html">cyclops.tasks.classification.BinaryTabularClassificationTask</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html">cyclops.tasks.classification.MultilabelImageClassificationTask</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/evaluator.html">Evaluator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/fairness_evaluator.html">Fairness Evaluator</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/metrics.html">Evaluation Metrics</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of Evaluation Metrics</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/accuracy.html">Accuracy</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/auroc.html">AUROC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/average_precision.html">Average Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f1score.html">F1-Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f_beta.html">F-beta Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision.html">Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision_recall_curve.html">PrecisionRecallCurve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/recall.html">Recall</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/roc.html">ROC Curve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/sensitivity.html">Sensitivity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/specificity.html">Specificity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/stat_scores.html">StatScores</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.monitor.html">cyclops.monitor</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of cyclops.monitor</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.html">cyclops.monitor.clinical_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of cyclops.monitor.clinical_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html">cyclops.monitor.clinical_applicator.ClinicalShiftApplicator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html">cyclops.monitor.synthetic_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of cyclops.monitor.synthetic_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html">cyclops.monitor.synthetic_applicator.binary_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html">cyclops.monitor.synthetic_applicator.feature_association_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html">cyclops.monitor.synthetic_applicator.feature_swap_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html">cyclops.monitor.synthetic_applicator.gaussian_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html">cyclops.monitor.synthetic_applicator.knockout_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html">cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.report.html">cyclops.report</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of cyclops.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.html">cyclops.report.report</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of cyclops.report.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.ModelCardReport.html">cyclops.report.report.ModelCardReport</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-</div>
-</div>
-
-      </div>
-      
-    </div>
-  </aside>
-  <div class="main">
-    <div class="content">
-      <div class="article-container">
-        <a href="#" class="back-to-top muted-link">
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
-            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
-          </svg>
-          <span>Back to top</span>
-        </a>
-        <div class="content-icon-container">
-          
-<div class="theme-toggle-container theme-toggle-content">
-            <button class="theme-toggle">
-              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-            </button>
-          </div>
-          <label class="toc-overlay-icon toc-content-icon" for="__toc">
-            <div class="visually-hidden">Toggle table of contents sidebar</div>
-            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-          </label>
-        </div>
-        <article role="main">
-          <section id="Chest-X-Ray-Disease-Classification">
-<h1>Chest X-Ray Disease Classification<a class="headerlink" href="#Chest-X-Ray-Disease-Classification" title="Link to this heading">#</a></h1>
-<p>This notebook shows chest x-ray classification on the <a class="reference external" href="https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community">NIH dataset</a> using a pretrained model from the TorchXRayVision library and CyclOps to generate a model card.</p>
-<section id="Import-Libraries">
-<h2>Import Libraries<a class="headerlink" href="#Import-Libraries" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;Chest X-ray Disease Classification.&quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">shutil</span>
-<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
-<span class="kn">from</span> <span class="nn">torchvision.transforms</span> <span class="kn">import</span> <span class="n">Compose</span>
-<span class="kn">from</span> <span class="nn">torchxrayvision.models</span> <span class="kn">import</span> <span class="n">DenseNet</span>
-
-<span class="kn">from</span> <span class="nn">cyclops.data.loader</span> <span class="kn">import</span> <span class="n">load_nihcxr</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.slicer</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">SliceSpec</span><span class="p">,</span>
-    <span class="n">filter_value</span><span class="p">,</span>  <span class="c1"># noqa: E402</span>
-<span class="p">)</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.transforms</span> <span class="kn">import</span> <span class="n">Lambdad</span><span class="p">,</span> <span class="n">Resized</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.utils</span> <span class="kn">import</span> <span class="n">apply_transforms</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate</span> <span class="kn">import</span> <span class="n">evaluator</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics.factory</span> <span class="kn">import</span> <span class="n">create_metric</span>
-<span class="kn">from</span> <span class="nn">cyclops.models.wrappers</span> <span class="kn">import</span> <span class="n">PTModel</span>
-<span class="kn">from</span> <span class="nn">cyclops.report</span> <span class="kn">import</span> <span class="n">ModelCardReport</span>
-</pre></div>
-</div>
-</div>
-<section id="Generate-Historical-Reports">
-<h3>Generate Historical Reports<a class="headerlink" href="#Generate-Historical-Reports" title="Link to this heading">#</a></h3>
-<p>CyclOps offers a package for documentation of the model through a model report. The <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> class is used to populate and generate the model report as an HTML file. The model report has the following sections:</p>
-<ul class="simple">
-<li><p>Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).</p></li>
-<li><p>Datasets: High level statistics of the training data, including changes in distribution over time.</p></li>
-<li><p>Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.</p></li>
-<li><p>Fairness Analysis: This section contains the fairness metrics of the model.</p></li>
-<li><p>Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.</p></li>
-<li><p>Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.</p></li>
-<li><p>Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.</p></li>
-</ul>
-<p>We will use this to document the model development process as we go along and generate the model report at the end.</p>
-<p><code class="docutils literal notranslate"><span class="pre">The</span> <span class="pre">model</span> <span class="pre">report</span> <span class="pre">tool</span> <span class="pre">is</span> <span class="pre">a</span> <span class="pre">work</span> <span class="pre">in</span> <span class="pre">progress</span> <span class="pre">and</span> <span class="pre">is</span> <span class="pre">subject</span> <span class="pre">to</span> <span class="pre">change.</span></code></p>
-</section>
-<section id="Initialize-Periodic-Report">
-<h3>Initialize Periodic Report<a class="headerlink" href="#Initialize-Periodic-Report" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">ModelCardReport</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="Load-Dataset">
-<h2>Load Dataset<a class="headerlink" href="#Load-Dataset" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data_dir</span> <span class="o">=</span> <span class="s2">&quot;/mnt/data/clinical_datasets/NIHCXR&quot;</span>
-<span class="n">nih_ds</span> <span class="o">=</span> <span class="n">load_nihcxr</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)[</span><span class="s2">&quot;test&quot;</span><span class="p">]</span>
-<span class="n">nih_ds</span> <span class="o">=</span> <span class="n">nih_ds</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">))</span>
-
-<span class="n">transforms</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">(</span>
-    <span class="p">[</span>
-        <span class="n">Resized</span><span class="p">(</span>
-            <span class="n">keys</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;image&quot;</span><span class="p">,),</span>
-            <span class="n">spatial_size</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
-            <span class="n">allow_missing_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="p">),</span>
-        <span class="n">Lambdad</span><span class="p">(</span>
-            <span class="n">keys</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;image&quot;</span><span class="p">,),</span>
-            <span class="n">func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">))</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">*</span> <span class="mi">1024</span><span class="p">,</span>
-            <span class="n">allow_missing_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="p">),</span>
-        <span class="n">Lambdad</span><span class="p">(</span>
-            <span class="n">keys</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;image&quot;</span><span class="p">,),</span>
-            <span class="n">func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span> <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">x</span><span class="p">,</span>
-            <span class="n">allow_missing_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="p">),</span>
-    <span class="p">],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<section id="Model-Creation">
-<h3>Model Creation<a class="headerlink" href="#Model-Creation" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">PTModel</span><span class="p">(</span><span class="n">DenseNet</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s2">&quot;densenet121-res224-nih&quot;</span><span class="p">))</span>
-
-<span class="n">model</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
-<span class="n">nih_ds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
-    <span class="n">nih_ds</span><span class="p">,</span>
-    <span class="n">feature_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">],</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span><span class="n">apply_transforms</span><span class="p">,</span> <span class="n">transforms</span><span class="o">=</span><span class="n">transforms</span><span class="p">),</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="s2">&quot;densenet&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># remove any rows with No Finding == 1</span>
-<span class="n">nih_ds</span> <span class="o">=</span> <span class="n">nih_ds</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span>
-    <span class="n">partial</span><span class="p">(</span><span class="n">filter_value</span><span class="p">,</span> <span class="n">column_name</span><span class="o">=</span><span class="s2">&quot;No Finding&quot;</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">negate</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-    <span class="n">batched</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># remove the No Finding column and adjust the predictions to account for it</span>
-<span class="n">nih_ds</span> <span class="o">=</span> <span class="n">nih_ds</span><span class="o">.</span><span class="n">map</span><span class="p">(</span>
-    <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">{</span>
-        <span class="s2">&quot;predictions.densenet&quot;</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;predictions.densenet&quot;</span><span class="p">][:</span><span class="mi">14</span><span class="p">],</span>
-    <span class="p">},</span>
-    <span class="n">remove_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;No Finding&quot;</span><span class="p">],</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">nih_ds</span><span class="o">.</span><span class="n">features</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="Multilabel-AUROC-by-Pathology-and-Sex">
-<h2>Multilabel AUROC by Pathology and Sex<a class="headerlink" href="#Multilabel-AUROC-by-Pathology-and-Sex" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pathologies</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">pathologies</span><span class="p">[:</span><span class="mi">14</span><span class="p">]</span>
-
-<span class="c1"># define the slices</span>
-<span class="n">slices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">{</span><span class="s2">&quot;Patient Gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">}},</span>
-    <span class="p">{</span><span class="s2">&quot;Patient Gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;F&quot;</span><span class="p">}},</span>
-<span class="p">]</span>
-
-
-<span class="n">num_labels</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pathologies</span><span class="p">)</span>
-<span class="n">ppv</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span>
-    <span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;multilabel_ppv&quot;</span><span class="p">,</span>
-    <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">num_labels</span><span class="o">=</span><span class="n">num_labels</span><span class="p">,</span>
-    <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">npv</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span>
-    <span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;multilabel_npv&quot;</span><span class="p">,</span>
-    <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">num_labels</span><span class="o">=</span><span class="n">num_labels</span><span class="p">,</span>
-    <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">specificity</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span>
-    <span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;multilabel_specificity&quot;</span><span class="p">,</span>
-    <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">num_labels</span><span class="o">=</span><span class="n">num_labels</span><span class="p">,</span>
-    <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">sensitivity</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span>
-    <span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;multilabel_sensitivity&quot;</span><span class="p">,</span>
-    <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">num_labels</span><span class="o">=</span><span class="n">num_labels</span><span class="p">,</span>
-    <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># create the slice functions</span>
-<span class="n">slice_spec</span> <span class="o">=</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="o">=</span><span class="n">slices</span><span class="p">)</span>
-
-<span class="n">nih_eval_results_gender</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="n">nih_ds</span><span class="p">,</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">ppv</span><span class="p">,</span> <span class="n">npv</span><span class="p">,</span> <span class="n">sensitivity</span><span class="p">,</span> <span class="n">specificity</span><span class="p">],</span>
-    <span class="n">target_columns</span><span class="o">=</span><span class="n">pathologies</span><span class="p">,</span>
-    <span class="n">prediction_columns</span><span class="o">=</span><span class="s2">&quot;predictions.densenet&quot;</span><span class="p">,</span>
-    <span class="n">ignore_columns</span><span class="o">=</span><span class="s2">&quot;image&quot;</span><span class="p">,</span>
-    <span class="n">slice_spec</span><span class="o">=</span><span class="n">slice_spec</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Multilabel-AUROC-by-Pathology-and-Age">
-<h2>Multilabel AUROC by Pathology and Age<a class="headerlink" href="#Multilabel-AUROC-by-Pathology-and-Age" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># define the slices</span>
-<span class="n">slices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">{</span><span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">19</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">35</span><span class="p">}},</span>
-    <span class="p">{</span><span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">35</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">65</span><span class="p">}},</span>
-    <span class="p">{</span><span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">65</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">100</span><span class="p">}},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">19</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">35</span><span class="p">},</span>
-        <span class="s2">&quot;Patient Gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">19</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">35</span><span class="p">},</span>
-        <span class="s2">&quot;Patient Gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;F&quot;</span><span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">35</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">65</span><span class="p">},</span>
-        <span class="s2">&quot;Patient Gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">35</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">65</span><span class="p">},</span>
-        <span class="s2">&quot;Patient Gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;F&quot;</span><span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">65</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">100</span><span class="p">},</span>
-        <span class="s2">&quot;Patient Gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">65</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">100</span><span class="p">},</span>
-        <span class="s2">&quot;Patient Gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;F&quot;</span><span class="p">},</span>
-    <span class="p">},</span>
-<span class="p">]</span>
-
-<span class="c1"># create the slice functions</span>
-<span class="n">slice_spec</span> <span class="o">=</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="o">=</span><span class="n">slices</span><span class="p">)</span>
-
-<span class="n">nih_eval_results_age</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="n">nih_ds</span><span class="p">,</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">ppv</span><span class="p">,</span> <span class="n">npv</span><span class="p">,</span> <span class="n">sensitivity</span><span class="p">,</span> <span class="n">specificity</span><span class="p">],</span>
-    <span class="n">target_columns</span><span class="o">=</span><span class="n">pathologies</span><span class="p">,</span>
-    <span class="n">prediction_columns</span><span class="o">=</span><span class="s2">&quot;predictions.densenet&quot;</span><span class="p">,</span>
-    <span class="n">ignore_columns</span><span class="o">=</span><span class="s2">&quot;image&quot;</span><span class="p">,</span>
-    <span class="n">slice_spec</span><span class="o">=</span><span class="n">slice_spec</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span>
-    <span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="n">nih_ds</span><span class="p">[</span><span class="s2">&quot;Patient Gender&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s2">&quot;M&quot;</span><span class="p">),</span> <span class="n">nih_ds</span><span class="p">[</span><span class="s2">&quot;Patient Gender&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s2">&quot;F&quot;</span><span class="p">)],</span>
-    <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Male&quot;</span><span class="p">,</span> <span class="s2">&quot;Female&quot;</span><span class="p">],</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Gender Distribution&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Gender Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">nih_ds</span><span class="p">[</span><span class="s2">&quot;Patient Age&quot;</span><span class="p">])</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span><span class="n">showlegend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">,</span>
-    <span class="n">bargap</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pathologies</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">nih_ds</span><span class="p">[</span><span class="n">p</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">pathologies</span><span class="p">])</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Pathology Distribution&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Pathology&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">,</span>
-    <span class="n">bargap</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-    <span class="c1"># change size of plot</span>
-<span class="p">)</span>
-
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Pathology Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Log-Performance-Metrics-as-Tests-w/-Thresholds">
-<h2>Log Performance Metrics as Tests w/ Thresholds<a class="headerlink" href="#Log-Performance-Metrics-as-Tests-w/-Thresholds" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results_flat</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="k">for</span> <span class="n">slice_</span><span class="p">,</span> <span class="n">metrics</span> <span class="ow">in</span> <span class="n">nih_eval_results_age</span><span class="p">[</span><span class="s2">&quot;model_for_predictions.densenet&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">results_flat</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">slice_</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">itr</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">metric</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">slice_</span> <span class="o">==</span> <span class="s2">&quot;overall&quot;</span><span class="p">:</span>
-                <span class="n">results_flat</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;pathology:</span><span class="si">{</span><span class="n">pathologies</span><span class="p">[</span><span class="n">itr</span><span class="p">]</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">results_flat</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">slice_</span><span class="si">}</span><span class="s2">&amp;pathology:</span><span class="si">{</span><span class="n">pathologies</span><span class="p">[</span><span class="n">itr</span><span class="p">]</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span>
-<span class="k">for</span> <span class="n">slice_</span><span class="p">,</span> <span class="n">metrics</span> <span class="ow">in</span> <span class="n">nih_eval_results_gender</span><span class="p">[</span>
-    <span class="s2">&quot;model_for_predictions.densenet&quot;</span>
-<span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">results_flat</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">slice_</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">itr</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">metric</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">slice_</span> <span class="o">==</span> <span class="s2">&quot;overall&quot;</span><span class="p">:</span>
-                <span class="n">results_flat</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;pathology:</span><span class="si">{</span><span class="n">pathologies</span><span class="p">[</span><span class="n">itr</span><span class="p">]</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">results_flat</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">slice_</span><span class="si">}</span><span class="s2">&amp;pathology:</span><span class="si">{</span><span class="n">pathologies</span><span class="p">[</span><span class="n">itr</span><span class="p">]</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span>
-
-<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">results_flat</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">split</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)</span>  <span class="c1"># noqa: PLW2901</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;MultilabelPPV&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of correctly predicted positive instances among all instances predicted as positive. Also known as precision.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;MultilabelNPV&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of correctly predicted negative instances among all instances predicted as negative.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;MultilabelSensitivity&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;MultilabelSpecificity&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of actual negative instances that are correctly predicted.&quot;</span><span class="p">,</span>
-    <span class="p">}</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">log_quantitative_analysis</span><span class="p">(</span>
-        <span class="s2">&quot;performance&quot;</span><span class="p">,</span>
-        <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
-        <span class="n">value</span><span class="o">=</span><span class="n">metric</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">generic</span><span class="p">)</span> <span class="k">else</span> <span class="n">metric</span><span class="p">,</span>
-        <span class="n">description</span><span class="o">=</span><span class="n">descriptions</span><span class="p">[</span><span class="n">name</span><span class="p">],</span>
-        <span class="n">metric_slice</span><span class="o">=</span><span class="n">split</span><span class="p">,</span>
-        <span class="n">pass_fail_thresholds</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
-        <span class="n">pass_fail_threshold_fns</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">),</span>
-    <span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<section id="Populate-Model-Card-Fields">
-<h3>Populate Model Card Fields<a class="headerlink" href="#Populate-Model-Card-Fields" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># model details for NIH Chest X-Ray model</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;NIH Chest X-Ray Multi-label Classification Model&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;This model is a DenseNet121 model trained on the NIH Chest </span><span class="se">\</span>
-<span class="s2">            X-Ray dataset, which contains 112,120 frontal-view X-ray images of 30,805 </span><span class="se">\</span>
-<span class="s2">            unique patients with the fourteen text-mined disease labels from the </span><span class="se">\</span>
-<span class="s2">            associated radiological reports. The labels are Atelectasis, Cardiomegaly, </span><span class="se">\</span>
-<span class="s2">            Effusion, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, </span><span class="se">\</span>
-<span class="s2">            Consolidation, Edema, Emphysema, Fibrosis, Pleural Thickening, and Hernia. </span><span class="se">\</span>
-<span class="s2">            The model was trained on 80</span><span class="si">% o</span><span class="s2">f the data and evaluated on the remaining </span><span class="se">\</span>
-<span class="s2">            20%.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;references&quot;</span><span class="p">:</span> <span class="p">[{</span><span class="s2">&quot;link&quot;</span><span class="p">:</span> <span class="s2">&quot;https://arxiv.org/abs/2111.00595&quot;</span><span class="p">}],</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;Model Details&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">report</span><span class="o">.</span><span class="n">log_citation</span><span class="p">(</span>
-    <span class="n">citation</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;@inproceedings{Cohen2022xrv,</span>
-<span class="s2">    title = {{TorchXRayVision: A library of chest X-ray datasets and models}},</span>
-<span class="s2">    author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, </span><span class="se">\</span>
-<span class="s2">    Paul and Morrison,Paul and Torabian, Parsa and Guarrera, </span><span class="se">\</span>
-<span class="s2">    Matteo and Lungren, Matthew P and Chaudhari,</span><span class="se">\</span>
-<span class="s2">    Akshay and Brooks, Rupert and Hashir, </span><span class="se">\</span>
-<span class="s2">    Mohammad and Bertrand, Hadrien},</span>
-<span class="s2">    booktitle = {Medical Imaging with Deep Learning},</span>
-<span class="s2">    url = {https://github.com/mlmed/torchxrayvision},</span>
-<span class="s2">    arxivId = </span><span class="si">{2111.00595}</span><span class="s2">,</span>
-<span class="s2">    year = </span><span class="si">{2022}</span>
-<span class="s2">    }&quot;&quot;&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">report</span><span class="o">.</span><span class="n">log_citation</span><span class="p">(</span>
-    <span class="n">citation</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;@inproceedings{cohen2020limits,</span>
-<span class="s2">    title={On the limits of cross-domain generalization</span><span class="se">\</span>
-<span class="s2">            in automated X-ray prediction},</span>
-<span class="s2">    author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, </span><span class="se">\</span>
-<span class="s2">        Rupert and Bertrand, Hadrien},</span>
-<span class="s2">    booktitle={Medical Imaging with Deep Learning},</span>
-<span class="s2">    year=</span><span class="si">{2020}</span><span class="s2">,</span>
-<span class="s2">    url={https://arxiv.org/abs/2002.02497}</span>
-<span class="s2">    }&quot;&quot;&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">report</span><span class="o">.</span><span class="n">log_owner</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;Machine Learning and Medicine Lab&quot;</span><span class="p">,</span>
-    <span class="n">contact</span><span class="o">=</span><span class="s2">&quot;mlmed.org&quot;</span><span class="p">,</span>
-    <span class="n">email</span><span class="o">=</span><span class="s2">&quot;joseph@josephpcohen.com&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># considerations</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_user</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;Radiologists&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_user</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;Data Scientists&quot;</span><span class="p">)</span>
-
-<span class="n">report</span><span class="o">.</span><span class="n">log_use_case</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;The model can be used to predict the presence of 14 pathologies </span><span class="se">\</span>
-<span class="s2">        in chest X-ray images.&quot;</span><span class="p">,</span>
-    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;primary&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_descriptor</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;limitations&quot;</span><span class="p">,</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;The limitations of this model include its inability to detect </span><span class="se">\</span>
-<span class="s2">                pathologies that are not included in the 14 labels of the NIH </span><span class="se">\</span>
-<span class="s2">                Chest X-Ray dataset. Additionally, the model may not perform </span><span class="se">\</span>
-<span class="s2">                well on images that are of poor quality or that contain </span><span class="se">\</span>
-<span class="s2">                artifacts. Finally, the model may not generalize well to</span><span class="se">\</span>
-<span class="s2">                populations that are not well-represented in the training </span><span class="se">\</span>
-<span class="s2">                data, such as patients from different geographic regions or </span><span class="se">\</span>
-<span class="s2">                with different demographics.&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;considerations&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_descriptor</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;tradeoffs&quot;</span><span class="p">,</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;The model can help radiologists to detect pathologies in </span><span class="se">\</span>
-<span class="s2">        chest X-ray images, but it may not generalize well to populations </span><span class="se">\</span>
-<span class="s2">        that are not well-represented in the training data.&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;considerations&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_risk</span><span class="p">(</span>
-    <span class="n">risk</span><span class="o">=</span><span class="s2">&quot;One ethical risk of the model is that it may not generalize well to </span><span class="se">\</span>
-<span class="s2">        populations that are not well-represented in the training data,</span><span class="se">\</span>
-<span class="s2">        such as patients from different geographic regions </span><span class="se">\</span>
-<span class="s2">        or with different demographics. &quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;A mitigation strategy for this risk is to ensure </span><span class="se">\</span>
-<span class="s2">        that the training data is diverse and representative of the population </span><span class="se">\</span>
-<span class="s2">            that the model will be used on. Additionally, the model should be </span><span class="se">\</span>
-<span class="s2">            regularly evaluated and updated to ensure that it continues to </span><span class="se">\</span>
-<span class="s2">            perform well on diverse populations. Finally, the model should </span><span class="se">\</span>
-<span class="s2">            be used in conjunction with human expertise to ensure that </span><span class="se">\</span>
-<span class="s2">            any biases or limitations are identified and addressed.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_fairness_assessment</span><span class="p">(</span>
-    <span class="n">affected_group</span><span class="o">=</span><span class="s2">&quot;Patients with rare pathologies&quot;</span><span class="p">,</span>
-    <span class="n">benefit</span><span class="o">=</span><span class="s2">&quot;The model can help radiologists to detect pathologies in </span><span class="se">\</span>
-<span class="s2">        chest X-ray images.&quot;</span><span class="p">,</span>
-    <span class="n">harm</span><span class="o">=</span><span class="s2">&quot;The model may not generalize well to populations that are not </span><span class="se">\</span>
-<span class="s2">        well-represented in the training data.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;A mitigation strategy for this risk is to ensure that </span><span class="se">\</span>
-<span class="s2">        the training data is diverse and representative of the population.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report_path</span> <span class="o">=</span> <span class="n">report</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
-    <span class="n">output_filename</span><span class="o">=</span><span class="s2">&quot;nihcxr_report_periodic.html&quot;</span><span class="p">,</span>
-    <span class="n">synthetic_timestamp</span><span class="o">=</span><span class="s2">&quot;2023-11-06&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>You can view the generated HTML <a class="reference external" href="./nihcxr_report_periodic.html">report</a>.</p>
-</section>
-</section>
-</section>
-
-        </article>
-      </div>
-      <footer>
-        
-        <div class="related-pages">
-          <a class="next-page" href="../../developing.html">
-              <div class="page-info">
-                <div class="context">
-                  <span>Next</span>
-                </div>
-                <div class="title">🧑🏿‍💻 Developing</div>
-              </div>
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-            </a>
-          <a class="prev-page" href="../diabetes_130/readmission_prediction.html">
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-              <div class="page-info">
-                <div class="context">
-                  <span>Previous</span>
-                </div>
-                
-                <div class="title">Readmission Prediction</div>
-                
-              </div>
-            </a>
-        </div>
-        <div class="bottom-of-page">
-          <div class="left-details">
-            <div class="copyright">
-                Copyright &#169; 2024, Vector AI Engineering
-            </div>
-            <!--
-            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-            
-            <a href="https://github.com/pradyunsg/furo">Furo</a>
-            
-            -->
-          </div>
-          <div class="right-details">
-            <div class="icons">
-              <a class="muted-link " href="https://github.com/VectorInstitute/cyclops" aria-label="GitHub">
-                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
-                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
-                </svg>
-            </a>
-              
-            </div>
-          </div>
-        </div>
-        <div class="footer-container" style="text-align: center;">
-          <div class="footer-small-text">
-              <br> All <a href="https://github.com/VectorInstitute/cyclops">source code for cyclops</a> is freely available under the terms of an <a href="https://opensource.org/licenses/Apache-2.0">Apache-2.0</a> license.
-          </div>
-
-          <div class="footer-small-text">
-            This work is made possible due to the data obtained from the <a href="https://www.geminimedicine.ca/">GEMINI study</a>.
-          </div>
-
-          <div class="logo">
-            <a class="logo" href="https://vectorinstitute.ai/">
-              <span class="logo"></span>
-              <img alt="Logo of the Vector Institute" style="width:90px; max-width:90px;" src="../../_static/logos/vector_logo.png" />
-            </a>
-            <a class="logo" href="https://www.geminimedicine.ca/">
-              <span class="logo"></span>
-              <img alt="Logo of GEMINI" style="width:90px; max-width:90px;" src="../../_static/logos/gemini_logo.png" />
-            </a>
-          </div>
-        </div>
-        
-      </footer>
-    </div>
-    <aside class="toc-drawer">
-      
-      
-      <div class="toc-sticky toc-scroll">
-        <div class="toc-title-container">
-          <span class="toc-title">
-            On this page
-          </span>
-        </div>
-        <div class="toc-tree-container">
-          <div class="toc-tree">
-            <ul>
-<li><a class="reference internal" href="#">Chest X-Ray Disease Classification</a><ul>
-<li><a class="reference internal" href="#Import-Libraries">Import Libraries</a><ul>
-<li><a class="reference internal" href="#Generate-Historical-Reports">Generate Historical Reports</a></li>
-<li><a class="reference internal" href="#Initialize-Periodic-Report">Initialize Periodic Report</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#Load-Dataset">Load Dataset</a><ul>
-<li><a class="reference internal" href="#Model-Creation">Model Creation</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#Multilabel-AUROC-by-Pathology-and-Sex">Multilabel AUROC by Pathology and Sex</a></li>
-<li><a class="reference internal" href="#Multilabel-AUROC-by-Pathology-and-Age">Multilabel AUROC by Pathology and Age</a></li>
-<li><a class="reference internal" href="#Log-Performance-Metrics-as-Tests-w/-Thresholds">Log Performance Metrics as Tests w/ Thresholds</a><ul>
-<li><a class="reference internal" href="#Populate-Model-Card-Fields">Populate Model Card Fields</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-          </div>
-        </div>
-      </div>
-      
-      
-    </aside>
-  </div>
-</div></body>
-</html>
\ No newline at end of file
diff --git a/api/tutorials/nihcxr/cxr_classification.ipynb b/api/tutorials/nihcxr/cxr_classification.ipynb
deleted file mode 100644
index e2cbed78e..000000000
--- a/api/tutorials/nihcxr/cxr_classification.ipynb
+++ /dev/null
@@ -1,611 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ea16a542",
-   "metadata": {},
-   "source": [
-    "# Chest X-Ray Disease Classification\n",
-    "\n",
-    "This notebook shows chest x-ray classification on the [NIH dataset](https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community) using a pretrained model from the TorchXRayVision library and CyclOps to generate a model card."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb698a85",
-   "metadata": {},
-   "source": [
-    "### Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fc1eb72a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"Chest X-ray Disease Classification.\"\"\"\n",
-    "\n",
-    "import shutil\n",
-    "from functools import partial\n",
-    "\n",
-    "import numpy as np\n",
-    "import plotly.express as px\n",
-    "from torchvision.transforms import Compose\n",
-    "from torchxrayvision.models import DenseNet\n",
-    "\n",
-    "from cyclops.data.loader import load_nihcxr\n",
-    "from cyclops.data.slicer import (\n",
-    "    SliceSpec,\n",
-    "    filter_value,  # noqa: E402\n",
-    ")\n",
-    "from cyclops.data.transforms import Lambdad, Resized\n",
-    "from cyclops.data.utils import apply_transforms\n",
-    "from cyclops.evaluate import evaluator\n",
-    "from cyclops.evaluate.metrics.factory import create_metric\n",
-    "from cyclops.models.wrappers import PTModel\n",
-    "from cyclops.report import ModelCardReport"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac67f0e7",
-   "metadata": {},
-   "source": [
-    "## Generate Historical Reports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25c2a16f",
-   "metadata": {
-    "nbsphinx": "hidden"
-   },
-   "outputs": [],
-   "source": [
-    "\"\"\"Generate historical reports with validation data\n",
-    "for comparison with periodic report on test data.\"\"\"\n",
-    "\n",
-    "!python3 generate_nihcxr_report.py --synthetic_timestamp \"2023-10-19\" --seed 43\n",
-    "!python3 generate_nihcxr_report.py --synthetic_timestamp \"2023-10-16\" --seed 44\n",
-    "!python3 generate_nihcxr_report.py --synthetic_timestamp \"2023-10-22\" --seed 45\n",
-    "!python3 generate_nihcxr_report.py --synthetic_timestamp \"2023-10-30\" --seed 46"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "198896c7",
-   "metadata": {},
-   "source": [
-    "CyclOps offers a package for documentation of the model through a model report. The `ModelCardReport` class is used to populate and generate the model report as an HTML file. The model report has the following sections:\n",
-    "\n",
-    "- Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).\n",
-    "- Datasets: High level statistics of the training data, including changes in distribution over time.\n",
-    "- Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.\n",
-    "- Fairness Analysis: This section contains the fairness metrics of the model.\n",
-    "- Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.\n",
-    "- Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.\n",
-    "- Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.\n",
-    "\n",
-    "We will use this to document the model development process as we go along and generate the model report at the end.\n",
-    "\n",
-    "`The model report tool is a work in progress and is subject to change.`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ee175a9",
-   "metadata": {},
-   "source": [
-    "## Initialize Periodic Report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "03edf1c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f8e8915",
-   "metadata": {},
-   "source": [
-    "### Load Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6514120e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dir = \"/mnt/data/clinical_datasets/NIHCXR\"\n",
-    "nih_ds = load_nihcxr(data_dir)[\"test\"]\n",
-    "nih_ds = nih_ds.select(range(1000))\n",
-    "\n",
-    "transforms = Compose(\n",
-    "    [\n",
-    "        Resized(\n",
-    "            keys=(\"image\",),\n",
-    "            spatial_size=(224, 224),\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "        Lambdad(\n",
-    "            keys=(\"image\",),\n",
-    "            func=lambda x: ((2 * (x / 255.0)) - 1.0) * 1024,\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "        Lambdad(\n",
-    "            keys=(\"image\",),\n",
-    "            func=lambda x: x[0][np.newaxis, :] if x.shape[0] != 1 else x,\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "    ],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7c9172a",
-   "metadata": {},
-   "source": [
-    "## Model Creation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5f624ed4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = PTModel(DenseNet(weights=\"densenet121-res224-nih\"))\n",
-    "\n",
-    "model.initialize()\n",
-    "nih_ds = model.predict(\n",
-    "    nih_ds,\n",
-    "    feature_columns=[\"image\"],\n",
-    "    transforms=partial(apply_transforms, transforms=transforms),\n",
-    "    model_name=\"densenet\",\n",
-    ")\n",
-    "\n",
-    "# remove any rows with No Finding == 1\n",
-    "nih_ds = nih_ds.filter(\n",
-    "    partial(filter_value, column_name=\"No Finding\", value=1, negate=True),\n",
-    "    batched=True,\n",
-    ")\n",
-    "\n",
-    "# remove the No Finding column and adjust the predictions to account for it\n",
-    "nih_ds = nih_ds.map(\n",
-    "    lambda x: {\n",
-    "        \"predictions.densenet\": x[\"predictions.densenet\"][:14],\n",
-    "    },\n",
-    "    remove_columns=[\"No Finding\"],\n",
-    ")\n",
-    "print(nih_ds.features)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9743a047",
-   "metadata": {},
-   "source": [
-    "### Multilabel AUROC by Pathology and Sex"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bff27cc1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pathologies = model.model.pathologies[:14]\n",
-    "\n",
-    "# define the slices\n",
-    "slices = [\n",
-    "    {\"Patient Gender\": {\"value\": \"M\"}},\n",
-    "    {\"Patient Gender\": {\"value\": \"F\"}},\n",
-    "]\n",
-    "\n",
-    "\n",
-    "num_labels = len(pathologies)\n",
-    "ppv = create_metric(\n",
-    "    metric_name=\"multilabel_ppv\",\n",
-    "    experimental=True,\n",
-    "    num_labels=num_labels,\n",
-    "    average=None,\n",
-    ")\n",
-    "\n",
-    "npv = create_metric(\n",
-    "    metric_name=\"multilabel_npv\",\n",
-    "    experimental=True,\n",
-    "    num_labels=num_labels,\n",
-    "    average=None,\n",
-    ")\n",
-    "\n",
-    "specificity = create_metric(\n",
-    "    metric_name=\"multilabel_specificity\",\n",
-    "    experimental=True,\n",
-    "    num_labels=num_labels,\n",
-    "    average=None,\n",
-    ")\n",
-    "\n",
-    "sensitivity = create_metric(\n",
-    "    metric_name=\"multilabel_sensitivity\",\n",
-    "    experimental=True,\n",
-    "    num_labels=num_labels,\n",
-    "    average=None,\n",
-    ")\n",
-    "\n",
-    "# create the slice functions\n",
-    "slice_spec = SliceSpec(spec_list=slices)\n",
-    "\n",
-    "nih_eval_results_gender = evaluator.evaluate(\n",
-    "    dataset=nih_ds,\n",
-    "    metrics=[ppv, npv, sensitivity, specificity],\n",
-    "    target_columns=pathologies,\n",
-    "    prediction_columns=\"predictions.densenet\",\n",
-    "    ignore_columns=\"image\",\n",
-    "    slice_spec=slice_spec,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "88448ced",
-   "metadata": {},
-   "source": [
-    "### Multilabel AUROC by Pathology and Age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c38ef9e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define the slices\n",
-    "slices = [\n",
-    "    {\"Patient Age\": {\"min_value\": 19, \"max_value\": 35}},\n",
-    "    {\"Patient Age\": {\"min_value\": 35, \"max_value\": 65}},\n",
-    "    {\"Patient Age\": {\"min_value\": 65, \"max_value\": 100}},\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 19, \"max_value\": 35},\n",
-    "        \"Patient Gender\": {\"value\": \"M\"},\n",
-    "    },\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 19, \"max_value\": 35},\n",
-    "        \"Patient Gender\": {\"value\": \"F\"},\n",
-    "    },\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 35, \"max_value\": 65},\n",
-    "        \"Patient Gender\": {\"value\": \"M\"},\n",
-    "    },\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 35, \"max_value\": 65},\n",
-    "        \"Patient Gender\": {\"value\": \"F\"},\n",
-    "    },\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 65, \"max_value\": 100},\n",
-    "        \"Patient Gender\": {\"value\": \"M\"},\n",
-    "    },\n",
-    "    {\n",
-    "        \"Patient Age\": {\"min_value\": 65, \"max_value\": 100},\n",
-    "        \"Patient Gender\": {\"value\": \"F\"},\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# create the slice functions\n",
-    "slice_spec = SliceSpec(spec_list=slices)\n",
-    "\n",
-    "nih_eval_results_age = evaluator.evaluate(\n",
-    "    dataset=nih_ds,\n",
-    "    metrics=[ppv, npv, sensitivity, specificity],\n",
-    "    target_columns=pathologies,\n",
-    "    prediction_columns=\"predictions.densenet\",\n",
-    "    ignore_columns=\"image\",\n",
-    "    slice_spec=slice_spec,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3e674b7a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.pie(\n",
-    "    values=[nih_ds[\"Patient Gender\"].count(\"M\"), nih_ds[\"Patient Gender\"].count(\"F\")],\n",
-    "    names=[\"Male\", \"Female\"],\n",
-    ")\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    title=\"Gender Distribution\",\n",
-    ")\n",
-    "\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Gender Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a144dc9a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(nih_ds[\"Patient Age\"])\n",
-    "fig.update_traces(showlegend=False)\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Age Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d2ff6506",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = px.bar(x=pathologies, y=[np.array(nih_ds[p]).sum() for p in pathologies])\n",
-    "fig.update_layout(\n",
-    "    title=\"Pathology Distribution\",\n",
-    "    xaxis_title=\"Pathology\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    "    # change size of plot\n",
-    ")\n",
-    "\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Pathology Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5eec4e5",
-   "metadata": {},
-   "source": [
-    "### Log Performance Metrics as Tests w/ Thresholds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec627997",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat = {}\n",
-    "for slice_, metrics in nih_eval_results_age[\"model_for_predictions.densenet\"].items():\n",
-    "    for name, metric in metrics.items():\n",
-    "        results_flat[f\"{slice_}/{name}\"] = metric.mean()\n",
-    "        for itr, m in enumerate(metric):\n",
-    "            if slice_ == \"overall\":\n",
-    "                results_flat[f\"pathology:{pathologies[itr]}/{name}\"] = m\n",
-    "            else:\n",
-    "                results_flat[f\"{slice_}&pathology:{pathologies[itr]}/{name}\"] = m\n",
-    "for slice_, metrics in nih_eval_results_gender[\n",
-    "    \"model_for_predictions.densenet\"\n",
-    "].items():\n",
-    "    for name, metric in metrics.items():\n",
-    "        results_flat[f\"{slice_}/{name}\"] = metric.mean()\n",
-    "        for itr, m in enumerate(metric):\n",
-    "            if slice_ == \"overall\":\n",
-    "                results_flat[f\"pathology:{pathologies[itr]}/{name}\"] = m\n",
-    "            else:\n",
-    "                results_flat[f\"{slice_}&pathology:{pathologies[itr]}/{name}\"] = m\n",
-    "\n",
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    descriptions = {\n",
-    "        \"MultilabelPPV\": \"The proportion of correctly predicted positive instances among all instances predicted as positive. Also known as precision.\",\n",
-    "        \"MultilabelNPV\": \"The proportion of correctly predicted negative instances among all instances predicted as negative.\",\n",
-    "        \"MultilabelSensitivity\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"MultilabelSpecificity\": \"The proportion of actual negative instances that are correctly predicted.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist() if isinstance(metric, np.generic) else metric,\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff0664e9",
-   "metadata": {},
-   "source": [
-    "## Populate Model Card Fields"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c9d6fe83",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# model details for NIH Chest X-Ray model\n",
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"NIH Chest X-Ray Multi-label Classification Model\",\n",
-    "        \"description\": \"This model is a DenseNet121 model trained on the NIH Chest \\\n",
-    "            X-Ray dataset, which contains 112,120 frontal-view X-ray images of 30,805 \\\n",
-    "            unique patients with the fourteen text-mined disease labels from the \\\n",
-    "            associated radiological reports. The labels are Atelectasis, Cardiomegaly, \\\n",
-    "            Effusion, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, \\\n",
-    "            Consolidation, Edema, Emphysema, Fibrosis, Pleural Thickening, and Hernia. \\\n",
-    "            The model was trained on 80% of the data and evaluated on the remaining \\\n",
-    "            20%.\",\n",
-    "        \"references\": [{\"link\": \"https://arxiv.org/abs/2111.00595\"}],\n",
-    "    },\n",
-    "    section_name=\"Model Details\",\n",
-    ")\n",
-    "\n",
-    "report.log_citation(\n",
-    "    citation=\"\"\"@inproceedings{Cohen2022xrv,\n",
-    "    title = {{TorchXRayVision: A library of chest X-ray datasets and models}},\n",
-    "    author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, \\\n",
-    "    Paul and Morrison,Paul and Torabian, Parsa and Guarrera, \\\n",
-    "    Matteo and Lungren, Matthew P and Chaudhari,\\\n",
-    "    Akshay and Brooks, Rupert and Hashir, \\\n",
-    "    Mohammad and Bertrand, Hadrien},\n",
-    "    booktitle = {Medical Imaging with Deep Learning},\n",
-    "    url = {https://github.com/mlmed/torchxrayvision},\n",
-    "    arxivId = {2111.00595},\n",
-    "    year = {2022}\n",
-    "    }\"\"\",\n",
-    ")\n",
-    "\n",
-    "report.log_citation(\n",
-    "    citation=\"\"\"@inproceedings{cohen2020limits,\n",
-    "    title={On the limits of cross-domain generalization\\\n",
-    "            in automated X-ray prediction},\n",
-    "    author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, \\\n",
-    "        Rupert and Bertrand, Hadrien},\n",
-    "    booktitle={Medical Imaging with Deep Learning},\n",
-    "    year={2020},\n",
-    "    url={https://arxiv.org/abs/2002.02497}\n",
-    "    }\"\"\",\n",
-    ")\n",
-    "\n",
-    "report.log_owner(\n",
-    "    name=\"Machine Learning and Medicine Lab\",\n",
-    "    contact=\"mlmed.org\",\n",
-    "    email=\"joseph@josephpcohen.com\",\n",
-    ")\n",
-    "\n",
-    "# considerations\n",
-    "report.log_user(description=\"Radiologists\")\n",
-    "report.log_user(description=\"Data Scientists\")\n",
-    "\n",
-    "report.log_use_case(\n",
-    "    description=\"The model can be used to predict the presence of 14 pathologies \\\n",
-    "        in chest X-ray images.\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_descriptor(\n",
-    "    name=\"limitations\",\n",
-    "    description=\"The limitations of this model include its inability to detect \\\n",
-    "                pathologies that are not included in the 14 labels of the NIH \\\n",
-    "                Chest X-Ray dataset. Additionally, the model may not perform \\\n",
-    "                well on images that are of poor quality or that contain \\\n",
-    "                artifacts. Finally, the model may not generalize well to\\\n",
-    "                populations that are not well-represented in the training \\\n",
-    "                data, such as patients from different geographic regions or \\\n",
-    "                with different demographics.\",\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_descriptor(\n",
-    "    name=\"tradeoffs\",\n",
-    "    description=\"The model can help radiologists to detect pathologies in \\\n",
-    "        chest X-ray images, but it may not generalize well to populations \\\n",
-    "        that are not well-represented in the training data.\",\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"One ethical risk of the model is that it may not generalize well to \\\n",
-    "        populations that are not well-represented in the training data,\\\n",
-    "        such as patients from different geographic regions \\\n",
-    "        or with different demographics. \",\n",
-    "    mitigation_strategy=\"A mitigation strategy for this risk is to ensure \\\n",
-    "        that the training data is diverse and representative of the population \\\n",
-    "            that the model will be used on. Additionally, the model should be \\\n",
-    "            regularly evaluated and updated to ensure that it continues to \\\n",
-    "            perform well on diverse populations. Finally, the model should \\\n",
-    "            be used in conjunction with human expertise to ensure that \\\n",
-    "            any biases or limitations are identified and addressed.\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"Patients with rare pathologies\",\n",
-    "    benefit=\"The model can help radiologists to detect pathologies in \\\n",
-    "        chest X-ray images.\",\n",
-    "    harm=\"The model may not generalize well to populations that are not \\\n",
-    "        well-represented in the training data.\",\n",
-    "    mitigation_strategy=\"A mitigation strategy for this risk is to ensure that \\\n",
-    "        the training data is diverse and representative of the population.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ae088e5b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report_path = report.export(\n",
-    "    output_filename=\"nihcxr_report_periodic.html\",\n",
-    "    synthetic_timestamp=\"2023-11-06\",\n",
-    ")\n",
-    "shutil.copy(f\"{report_path}\", \".\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7545d39",
-   "metadata": {},
-   "source": [
-    "You can view the generated HTML [report](./nihcxr_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/api/tutorials/nihcxr/monitor_api.html b/api/tutorials/nihcxr/monitor_api.html
deleted file mode 100644
index a237c43b1..000000000
--- a/api/tutorials/nihcxr/monitor_api.html
+++ /dev/null
@@ -1,642 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en">
-  <head><meta charset="utf-8"/>
-    <meta name="viewport" content="width=device-width,initial-scale=1"/>
-    <meta name="color-scheme" content="light dark">
-    <meta name="google-site-verification" content="i0qQRaR9OA3tSz_9tDocdcXGY27Ox_cy4FrvTHD2C_0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-
-    
-      <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" />
-
-    <link rel="shortcut icon" href="../../_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
-
-    
-    <meta name="msvalidate.01" content="8D2A5032F006424CAF54C0DDCD16F666" />
-      
-        <title>NIHCXR Clinical Drift Experiments Tutorial - cyclops documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
-    <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="../../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
-    <link rel="stylesheet" type="text/css" href="../../_static/nbsphinx-code-cells.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/css/cyclops.css?v=9f20b50b" />
-    
-    
-
-
-<style>
-  body {
-    --color-code-background: #f8f8f8;
-  --color-code-foreground: black;
-  --color-card-background: #5e88f2;
-  
-  }
-  @media not print {
-    body[data-theme="dark"] {
-      --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-    }
-    @media (prefers-color-scheme: dark) {
-      body:not([data-theme="light"]) {
-        --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-      }
-    }
-  }
-</style><script src="../../_static/documentation_options.js?v=5929fcd5"></script>
-    <script src="../../_static/doctools.js?v=888ff710"></script>
-    <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="../../_static/scripts/furo.js?v=32e29ea5"></script>
-    <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
-    <script src="../../_static/copybutton.js?v=ccdb6887"></script>
-    <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-    <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script src="../../_static/require.min.js?v=53367eec"></script>
-    <script src="../../_static/custom.js?v=275847af"></script>
-    
-    <script src="https://kit.fontawesome.com/2c1f516901.js" crossorigin="anonymous"></script>
-
-  </head>
-  <body>
-    
-    <script>
-      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
-    </script>
-    
-
-<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
-  <symbol id="svg-toc" viewBox="0 0 24 24">
-    <title>Contents</title>
-    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
-      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-menu" viewBox="0 0 24 24">
-    <title>Menu</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
-      <line x1="3" y1="12" x2="21" y2="12"></line>
-      <line x1="3" y1="6" x2="21" y2="6"></line>
-      <line x1="3" y1="18" x2="21" y2="18"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
-    <title>Expand</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
-      <polyline points="9 18 15 12 9 6"></polyline>
-    </svg>
-  </symbol>
-  <symbol id="svg-sun" viewBox="0 0 24 24">
-    <title>Light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
-      <circle cx="12" cy="12" r="5"></circle>
-      <line x1="12" y1="1" x2="12" y2="3"></line>
-      <line x1="12" y1="21" x2="12" y2="23"></line>
-      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
-      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
-      <line x1="1" y1="12" x2="3" y2="12"></line>
-      <line x1="21" y1="12" x2="23" y2="12"></line>
-      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
-      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon" viewBox="0 0 24 24">
-    <title>Dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
-      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
-    </svg>
-  </symbol>
-  <symbol id="svg-sun-half" viewBox="0 0 24 24">
-    <title>Auto light/dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-      <circle cx="12" cy="12" r="9" />
-      <path d="M13 12h5" />
-      <path d="M13 15h4" />
-      <path d="M13 18h1" />
-      <path d="M13 9h4" />
-      <path d="M13 6h1" />
-    </svg>
-  </symbol>
-</svg>
-
-<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
-<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
-<label class="overlay sidebar-overlay" for="__navigation">
-  <div class="visually-hidden">Hide navigation sidebar</div>
-</label>
-<label class="overlay toc-overlay" for="__toc">
-  <div class="visually-hidden">Hide table of contents sidebar</div>
-</label>
-
-
-
-<div class="page">
-  <header class="mobile-header">
-    <div class="header-left">
-      <label class="nav-overlay-icon" for="__navigation">
-        <div class="visually-hidden">Toggle site navigation sidebar</div>
-        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
-      </label>
-    </div>
-    <div class="header-center">
-      <a href="../../index.html"><div class="brand">cyclops  documentation</div></a>
-    </div>
-    <div class="header-right">
-      <div class="theme-toggle-container theme-toggle-header">
-        <button class="theme-toggle">
-          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-        </button>
-      </div>
-      <label class="toc-overlay-icon toc-header-icon" for="__toc">
-        <div class="visually-hidden">Toggle table of contents sidebar</div>
-        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-      </label>
-    </div>
-  </header>
-  <aside class="sidebar-drawer">
-    <div class="sidebar-container">
-      
-      <div class="sidebar-sticky"><a class="sidebar-brand" href="../../index.html">
-  
-  <div class="sidebar-logo-container">
-    <img class="sidebar-logo" src="../../_static/cyclops_logo-dark.png" alt="Logo"/>
-  </div>
-  
-  
-</a><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
-  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
-  <input type="hidden" name="check_keywords" value="yes">
-  <input type="hidden" name="area" value="default">
-</form>
-<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <ul>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
-</ul>
-</li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="cxr_classification.html">Chest X-Ray Disease Classification</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../developing.html">🧑🏿‍💻 Developing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing to cyclops</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../api.html">API Reference</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of API Reference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.data.html">cyclops.data</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of cyclops.data</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.html">cyclops.data.slicer</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of cyclops.data.slicer</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.compound_filter.html">cyclops.data.slicer.compound_filter</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html">cyclops.data.slicer.filter_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html">cyclops.data.slicer.filter_non_null</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_range.html">cyclops.data.slicer.filter_range</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html">cyclops.data.slicer.filter_string_contains</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_value.html">cyclops.data.slicer.filter_value</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.is_datetime.html">cyclops.data.slicer.is_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.overall.html">cyclops.data.slicer.overall</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html">cyclops.data.slicer.SliceSpec</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.html">cyclops.data.aggregate</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of cyclops.data.aggregate</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html">cyclops.data.aggregate.tabular_as_aggregated</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html">cyclops.data.aggregate.timestamp_ffill_agg</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html">cyclops.data.aggregate.Aggregator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.html">cyclops.data.features.medical_image</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of cyclops.data.features.medical_image</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html">cyclops.data.features.medical_image.MedicalImage</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.tasks.html">cyclops.tasks</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of cyclops.tasks</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.html">cyclops.tasks.classification</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of cyclops.tasks.classification</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html">cyclops.tasks.classification.BinaryTabularClassificationTask</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html">cyclops.tasks.classification.MultilabelImageClassificationTask</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/evaluator.html">Evaluator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/fairness_evaluator.html">Fairness Evaluator</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/metrics.html">Evaluation Metrics</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of Evaluation Metrics</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/accuracy.html">Accuracy</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/auroc.html">AUROC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/average_precision.html">Average Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f1score.html">F1-Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f_beta.html">F-beta Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision.html">Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision_recall_curve.html">PrecisionRecallCurve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/recall.html">Recall</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/roc.html">ROC Curve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/sensitivity.html">Sensitivity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/specificity.html">Specificity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/stat_scores.html">StatScores</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.monitor.html">cyclops.monitor</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of cyclops.monitor</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.html">cyclops.monitor.clinical_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of cyclops.monitor.clinical_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html">cyclops.monitor.clinical_applicator.ClinicalShiftApplicator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html">cyclops.monitor.synthetic_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of cyclops.monitor.synthetic_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html">cyclops.monitor.synthetic_applicator.binary_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html">cyclops.monitor.synthetic_applicator.feature_association_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html">cyclops.monitor.synthetic_applicator.feature_swap_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html">cyclops.monitor.synthetic_applicator.gaussian_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html">cyclops.monitor.synthetic_applicator.knockout_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html">cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.report.html">cyclops.report</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of cyclops.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.html">cyclops.report.report</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of cyclops.report.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.ModelCardReport.html">cyclops.report.report.ModelCardReport</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-</div>
-</div>
-
-      </div>
-      
-    </div>
-  </aside>
-  <div class="main">
-    <div class="content">
-      <div class="article-container">
-        <a href="#" class="back-to-top muted-link">
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
-            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
-          </svg>
-          <span>Back to top</span>
-        </a>
-        <div class="content-icon-container">
-          
-<div class="theme-toggle-container theme-toggle-content">
-            <button class="theme-toggle">
-              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-            </button>
-          </div>
-          <label class="toc-overlay-icon toc-content-icon" for="__toc">
-            <div class="visually-hidden">Toggle table of contents sidebar</div>
-            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-          </label>
-        </div>
-        <article role="main">
-          <section id="NIHCXR-Clinical-Drift-Experiments-Tutorial">
-<h1>NIHCXR Clinical Drift Experiments Tutorial<a class="headerlink" href="#NIHCXR-Clinical-Drift-Experiments-Tutorial" title="Link to this heading">#</a></h1>
-<section id="Import-Libraries-and-Load-NIHCXR-Dataset">
-<h2>Import Libraries and Load NIHCXR Dataset<a class="headerlink" href="#Import-Libraries-and-Load-NIHCXR-Dataset" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;NIHCXR Clinical Drift Experiments Tutorial.&quot;&quot;&quot;</span>
-
-
-<span class="kn">import</span> <span class="nn">random</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torchvision.transforms</span> <span class="kn">import</span> <span class="n">Compose</span>
-<span class="kn">from</span> <span class="nn">torchxrayvision.models</span> <span class="kn">import</span> <span class="n">DenseNet</span>
-
-<span class="kn">from</span> <span class="nn">cyclops.data.loader</span> <span class="kn">import</span> <span class="n">load_nihcxr</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.slicer</span> <span class="kn">import</span> <span class="n">SliceSpec</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.transforms</span> <span class="kn">import</span> <span class="n">Lambdad</span><span class="p">,</span> <span class="n">Resized</span>
-<span class="kn">from</span> <span class="nn">cyclops.monitor</span> <span class="kn">import</span> <span class="n">ClinicalShiftApplicator</span><span class="p">,</span> <span class="n">Detector</span><span class="p">,</span> <span class="n">Reductor</span><span class="p">,</span> <span class="n">TSTester</span>
-<span class="kn">from</span> <span class="nn">cyclops.monitor.plotter</span> <span class="kn">import</span> <span class="n">plot_drift_experiment</span><span class="p">,</span> <span class="n">plot_drift_timeseries</span>
-
-
-<span class="n">nih_ds</span> <span class="o">=</span> <span class="n">load_nihcxr</span><span class="p">(</span><span class="s2">&quot;/mnt/data/clinical_datasets/NIHCXR&quot;</span><span class="p">)[</span><span class="s2">&quot;test&quot;</span><span class="p">]</span>
-
-<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)">
-<h2>Example 1. Generate Source/Target Dataset for Experiments (1-2)<a class="headerlink" href="#Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">shifter</span> <span class="o">=</span> <span class="n">ClinicalShiftApplicator</span><span class="p">(</span>
-    <span class="s2">&quot;sex&quot;</span><span class="p">,</span>
-    <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">target</span><span class="o">=</span><span class="s2">&quot;F&quot;</span><span class="p">,</span>
-    <span class="n">shift_id</span><span class="o">=</span><span class="s2">&quot;Patient Gender&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">source_ds</span><span class="p">,</span> <span class="n">target_ds</span> <span class="o">=</span> <span class="n">shifter</span><span class="o">.</span><span class="n">apply_shift</span><span class="p">(</span><span class="n">nih_ds</span><span class="p">,</span> <span class="n">num_proc</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
-
-<span class="n">transforms</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">(</span>
-    <span class="p">[</span>
-        <span class="n">Resized</span><span class="p">(</span>
-            <span class="n">spatial_size</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
-            <span class="n">keys</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;image&quot;</span><span class="p">,),</span>
-            <span class="n">allow_missing_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="p">),</span>
-        <span class="n">Lambdad</span><span class="p">(</span>
-            <span class="n">func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">))</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">*</span> <span class="mi">1024</span><span class="p">,</span>
-            <span class="n">keys</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;image&quot;</span><span class="p">,),</span>
-            <span class="n">allow_missing_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="p">),</span>
-        <span class="n">Lambdad</span><span class="p">(</span>
-            <span class="n">func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span> <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">x</span><span class="p">,</span>
-            <span class="n">keys</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;image&quot;</span><span class="p">,),</span>
-            <span class="n">allow_missing_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="p">),</span>
-    <span class="p">],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques">
-<h2>Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques<a class="headerlink" href="#Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">DenseNet</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s2">&quot;densenet121-res224-all&quot;</span><span class="p">)</span>
-<span class="n">dr_methods</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;BBSE&quot;</span><span class="p">:</span> <span class="s2">&quot;bbse-soft&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;BBSE + TXRV-AE&quot;</span><span class="p">:</span> <span class="s2">&quot;bbse-soft+txrv-ae&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;TXRV-AE&quot;</span><span class="p">:</span> <span class="s2">&quot;txrv-ae&quot;</span><span class="p">,</span>
-<span class="p">}</span>
-<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
-
-<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">dr_method</span> <span class="ow">in</span> <span class="n">dr_methods</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;TXRV-AE&quot;</span><span class="p">:</span>
-        <span class="n">reductor</span> <span class="o">=</span> <span class="n">Reductor</span><span class="p">(</span>
-            <span class="n">dr_method</span><span class="o">=</span><span class="n">dr_method</span><span class="p">,</span>
-            <span class="n">transforms</span><span class="o">=</span><span class="n">transforms</span><span class="p">,</span>
-            <span class="n">feature_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">],</span>
-        <span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">reductor</span> <span class="o">=</span> <span class="n">Reductor</span><span class="p">(</span>
-            <span class="n">dr_method</span><span class="o">=</span><span class="n">dr_method</span><span class="p">,</span>
-            <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
-            <span class="n">transforms</span><span class="o">=</span><span class="n">transforms</span><span class="p">,</span>
-            <span class="n">feature_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">],</span>
-        <span class="p">)</span>
-    <span class="n">detector</span> <span class="o">=</span> <span class="n">Detector</span><span class="p">(</span>
-        <span class="s2">&quot;sensitivity_test&quot;</span><span class="p">,</span>
-        <span class="n">reductor</span><span class="o">=</span><span class="n">reductor</span><span class="p">,</span>
-        <span class="n">tester</span><span class="o">=</span><span class="n">TSTester</span><span class="p">(</span><span class="n">tester_method</span><span class="o">=</span><span class="s2">&quot;ks&quot;</span><span class="p">),</span>
-        <span class="n">source_sample_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-        <span class="n">target_sample_size</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">50</span><span class="p">],</span>
-        <span class="n">num_runs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="n">detector</span><span class="o">.</span><span class="n">detect_shift</span><span class="p">(</span><span class="n">source_ds</span><span class="p">,</span> <span class="n">target_ds</span><span class="p">)</span>
-    <span class="n">results</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span>
-<span class="n">plot_drift_experiment</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets">
-<h2>Example 3. Sensitivity test experiment with models trained on different datasets<a class="headerlink" href="#Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">models</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;MODEL: NIH&quot;</span><span class="p">:</span> <span class="s2">&quot;densenet121-res224-nih&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;MODEL: CHEXPERT&quot;</span><span class="p">:</span> <span class="s2">&quot;densenet121-res224-chex&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;MODEL: PADCHEST&quot;</span><span class="p">:</span> <span class="s2">&quot;densenet121-res224-pc&quot;</span><span class="p">,</span>
-<span class="p">}</span>
-<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
-
-<span class="k">for</span> <span class="n">model_name</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">detector</span> <span class="o">=</span> <span class="n">Detector</span><span class="p">(</span>
-        <span class="s2">&quot;sensitivity_test&quot;</span><span class="p">,</span>
-        <span class="n">reductor</span><span class="o">=</span><span class="n">Reductor</span><span class="p">(</span>
-            <span class="n">dr_method</span><span class="o">=</span><span class="s2">&quot;bbse-soft&quot;</span><span class="p">,</span>
-            <span class="n">model</span><span class="o">=</span><span class="n">DenseNet</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="n">model</span><span class="p">),</span>
-            <span class="n">transforms</span><span class="o">=</span><span class="n">transforms</span><span class="p">,</span>
-            <span class="n">feature_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">],</span>
-        <span class="p">),</span>
-        <span class="n">tester</span><span class="o">=</span><span class="n">TSTester</span><span class="p">(</span><span class="n">tester_method</span><span class="o">=</span><span class="s2">&quot;ks&quot;</span><span class="p">),</span>
-        <span class="n">source_sample_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-        <span class="n">target_sample_size</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">50</span><span class="p">],</span>
-        <span class="n">num_runs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">detector</span><span class="o">.</span><span class="n">detect_shift</span><span class="p">(</span><span class="n">source_ds</span><span class="p">,</span> <span class="n">target_ds</span><span class="p">)</span>
-<span class="n">plot_drift_experiment</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts">
-<h2>Example 4. Sensitivity test experiment with different clinical shifts<a class="headerlink" href="#Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">DenseNet</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s2">&quot;densenet121-res224-all&quot;</span><span class="p">)</span>
-<span class="n">source_slice</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="n">target_slices</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;SEX: MALE&quot;</span><span class="p">:</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="o">=</span><span class="p">[{</span><span class="s2">&quot;Patient Gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">}}]),</span>
-    <span class="s2">&quot;SEX: FEMALE&quot;</span><span class="p">:</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="o">=</span><span class="p">[{</span><span class="s2">&quot;Patient Gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;F&quot;</span><span class="p">}}]),</span>
-    <span class="s2">&quot;AGE: 18-35&quot;</span><span class="p">:</span> <span class="n">SliceSpec</span><span class="p">(</span>
-        <span class="n">spec_list</span><span class="o">=</span><span class="p">[{</span><span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">18</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">35</span><span class="p">}}],</span>
-    <span class="p">),</span>
-    <span class="s2">&quot;AGE: 35-65&quot;</span><span class="p">:</span> <span class="n">SliceSpec</span><span class="p">(</span>
-        <span class="n">spec_list</span><span class="o">=</span><span class="p">[{</span><span class="s2">&quot;Patient Age&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">35</span><span class="p">,</span> <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">65</span><span class="p">}}],</span>
-    <span class="p">),</span>
-<span class="p">}</span>
-<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
-
-<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">target_slice</span> <span class="ow">in</span> <span class="n">target_slices</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">source_slice</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="n">shifter</span> <span class="o">=</span> <span class="n">ClinicalShiftApplicator</span><span class="p">(</span>
-        <span class="s2">&quot;custom&quot;</span><span class="p">,</span>
-        <span class="n">source</span><span class="o">=</span><span class="n">source_slice</span><span class="p">,</span>
-        <span class="n">target</span><span class="o">=</span><span class="n">target_slice</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">ds_source</span><span class="p">,</span> <span class="n">ds_target</span> <span class="o">=</span> <span class="n">shifter</span><span class="o">.</span><span class="n">apply_shift</span><span class="p">(</span><span class="n">nih_ds</span><span class="p">,</span> <span class="n">num_proc</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
-
-    <span class="n">detector</span> <span class="o">=</span> <span class="n">Detector</span><span class="p">(</span>
-        <span class="s2">&quot;sensitivity_test&quot;</span><span class="p">,</span>
-        <span class="n">reductor</span><span class="o">=</span><span class="n">Reductor</span><span class="p">(</span>
-            <span class="n">dr_method</span><span class="o">=</span><span class="s2">&quot;bbse-soft&quot;</span><span class="p">,</span>
-            <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
-            <span class="n">transforms</span><span class="o">=</span><span class="n">transforms</span><span class="p">,</span>
-            <span class="n">feature_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">],</span>
-        <span class="p">),</span>
-        <span class="n">tester</span><span class="o">=</span><span class="n">TSTester</span><span class="p">(</span><span class="n">tester_method</span><span class="o">=</span><span class="s2">&quot;ks&quot;</span><span class="p">),</span>
-        <span class="n">source_sample_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-        <span class="n">target_sample_size</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">50</span><span class="p">],</span>
-        <span class="n">num_runs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">results</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">detector</span><span class="o">.</span><span class="n">detect_shift</span><span class="p">(</span><span class="n">ds_source</span><span class="p">,</span> <span class="n">ds_target</span><span class="p">)</span>
-<span class="n">plot_drift_experiment</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window">
-<h2>Example 5. Rolling window experiment with synthetic timestamps using biweekly window<a class="headerlink" href="#Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">DenseNet</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s2">&quot;densenet121-res224-all&quot;</span><span class="p">)</span>
-<span class="n">detector</span> <span class="o">=</span> <span class="n">Detector</span><span class="p">(</span>
-    <span class="s2">&quot;rolling_window_drift&quot;</span><span class="p">,</span>
-    <span class="n">reductor</span><span class="o">=</span><span class="n">Reductor</span><span class="p">(</span>
-        <span class="n">dr_method</span><span class="o">=</span><span class="s2">&quot;bbse-soft&quot;</span><span class="p">,</span>
-        <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
-        <span class="n">transforms</span><span class="o">=</span><span class="n">transforms</span><span class="p">,</span>
-        <span class="n">feature_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">],</span>
-    <span class="p">),</span>
-    <span class="n">tester</span><span class="o">=</span><span class="n">TSTester</span><span class="p">(</span><span class="n">tester_method</span><span class="o">=</span><span class="s2">&quot;ks&quot;</span><span class="p">),</span>
-    <span class="n">source_sample_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-    <span class="n">target_sample_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">timestamp_column</span><span class="o">=</span><span class="s2">&quot;timestamp&quot;</span><span class="p">,</span>
-    <span class="n">window_size</span><span class="o">=</span><span class="s2">&quot;4W&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="n">results</span> <span class="o">=</span> <span class="n">detector</span><span class="o">.</span><span class="n">detect_shift</span><span class="p">(</span><span class="n">source_ds</span><span class="p">,</span> <span class="n">target_ds</span><span class="p">)</span>
-<span class="n">plot_drift_timeseries</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-
-        </article>
-      </div>
-      <footer>
-        
-        <div class="related-pages">
-          
-          
-        </div>
-        <div class="bottom-of-page">
-          <div class="left-details">
-            <div class="copyright">
-                Copyright &#169; 2024, Vector AI Engineering
-            </div>
-            <!--
-            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-            
-            <a href="https://github.com/pradyunsg/furo">Furo</a>
-            
-            -->
-          </div>
-          <div class="right-details">
-            <div class="icons">
-              <a class="muted-link " href="https://github.com/VectorInstitute/cyclops" aria-label="GitHub">
-                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
-                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
-                </svg>
-            </a>
-              
-            </div>
-          </div>
-        </div>
-        <div class="footer-container" style="text-align: center;">
-          <div class="footer-small-text">
-              <br> All <a href="https://github.com/VectorInstitute/cyclops">source code for cyclops</a> is freely available under the terms of an <a href="https://opensource.org/licenses/Apache-2.0">Apache-2.0</a> license.
-          </div>
-
-          <div class="footer-small-text">
-            This work is made possible due to the data obtained from the <a href="https://www.geminimedicine.ca/">GEMINI study</a>.
-          </div>
-
-          <div class="logo">
-            <a class="logo" href="https://vectorinstitute.ai/">
-              <span class="logo"></span>
-              <img alt="Logo of the Vector Institute" style="width:90px; max-width:90px;" src="../../_static/logos/vector_logo.png" />
-            </a>
-            <a class="logo" href="https://www.geminimedicine.ca/">
-              <span class="logo"></span>
-              <img alt="Logo of GEMINI" style="width:90px; max-width:90px;" src="../../_static/logos/gemini_logo.png" />
-            </a>
-          </div>
-        </div>
-        
-      </footer>
-    </div>
-    <aside class="toc-drawer">
-      
-      
-      <div class="toc-sticky toc-scroll">
-        <div class="toc-title-container">
-          <span class="toc-title">
-            On this page
-          </span>
-        </div>
-        <div class="toc-tree-container">
-          <div class="toc-tree">
-            <ul>
-<li><a class="reference internal" href="#">NIHCXR Clinical Drift Experiments Tutorial</a><ul>
-<li><a class="reference internal" href="#Import-Libraries-and-Load-NIHCXR-Dataset">Import Libraries and Load NIHCXR Dataset</a></li>
-<li><a class="reference internal" href="#Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)">Example 1. Generate Source/Target Dataset for Experiments (1-2)</a></li>
-<li><a class="reference internal" href="#Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques">Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques</a></li>
-<li><a class="reference internal" href="#Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets">Example 3. Sensitivity test experiment with models trained on different datasets</a></li>
-<li><a class="reference internal" href="#Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts">Example 4. Sensitivity test experiment with different clinical shifts</a></li>
-<li><a class="reference internal" href="#Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window">Example 5. Rolling window experiment with synthetic timestamps using biweekly window</a></li>
-</ul>
-</li>
-</ul>
-
-          </div>
-        </div>
-      </div>
-      
-      
-    </aside>
-  </div>
-</div></body>
-</html>
\ No newline at end of file
diff --git a/api/tutorials/nihcxr/monitor_api.ipynb b/api/tutorials/nihcxr/monitor_api.ipynb
deleted file mode 100644
index 53e9ebcd3..000000000
--- a/api/tutorials/nihcxr/monitor_api.ipynb
+++ /dev/null
@@ -1,301 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6b2520a8-d4ad-4941-8ea7-71fdd631225f",
-   "metadata": {},
-   "source": [
-    "# NIHCXR Clinical Drift Experiments Tutorial"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "275fdb05",
-   "metadata": {},
-   "source": [
-    "## Import Libraries and Load NIHCXR Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8aa3302d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"NIHCXR Clinical Drift Experiments Tutorial.\"\"\"\n",
-    "\n",
-    "\n",
-    "import random\n",
-    "\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "from torchvision.transforms import Compose\n",
-    "from torchxrayvision.models import DenseNet\n",
-    "\n",
-    "from cyclops.data.loader import load_nihcxr\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.data.transforms import Lambdad, Resized\n",
-    "from cyclops.monitor import ClinicalShiftApplicator, Detector, Reductor, TSTester\n",
-    "from cyclops.monitor.plotter import plot_drift_experiment, plot_drift_timeseries\n",
-    "\n",
-    "\n",
-    "nih_ds = load_nihcxr(\"/mnt/data/clinical_datasets/NIHCXR\")[\"test\"]\n",
-    "\n",
-    "random.seed(42)\n",
-    "np.random.seed(42)\n",
-    "torch.manual_seed(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebcc72bb",
-   "metadata": {},
-   "source": [
-    "## Example 1. Generate Source/Target Dataset for Experiments (1-2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e11920db",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "shifter = ClinicalShiftApplicator(\n",
-    "    \"sex\",\n",
-    "    source=None,\n",
-    "    target=\"F\",\n",
-    "    shift_id=\"Patient Gender\",\n",
-    ")\n",
-    "source_ds, target_ds = shifter.apply_shift(nih_ds, num_proc=6)\n",
-    "\n",
-    "transforms = Compose(\n",
-    "    [\n",
-    "        Resized(\n",
-    "            spatial_size=(224, 224),\n",
-    "            keys=(\"image\",),\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "        Lambdad(\n",
-    "            func=lambda x: ((2 * (x / 255.0)) - 1.0) * 1024,\n",
-    "            keys=(\"image\",),\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "        Lambdad(\n",
-    "            func=lambda x: x[0][np.newaxis, :] if x.shape[0] != 1 else x,\n",
-    "            keys=(\"image\",),\n",
-    "            allow_missing_keys=True,\n",
-    "        ),\n",
-    "    ],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ab403dc5",
-   "metadata": {},
-   "source": [
-    "## Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "54a3523a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = DenseNet(weights=\"densenet121-res224-all\")\n",
-    "dr_methods = {\n",
-    "    \"BBSE\": \"bbse-soft\",\n",
-    "    \"BBSE + TXRV-AE\": \"bbse-soft+txrv-ae\",\n",
-    "    \"TXRV-AE\": \"txrv-ae\",\n",
-    "}\n",
-    "results = {}\n",
-    "\n",
-    "for name, dr_method in dr_methods.items():\n",
-    "    if name == \"TXRV-AE\":\n",
-    "        reductor = Reductor(\n",
-    "            dr_method=dr_method,\n",
-    "            transforms=transforms,\n",
-    "            feature_columns=[\"image\"],\n",
-    "        )\n",
-    "    else:\n",
-    "        reductor = Reductor(\n",
-    "            dr_method=dr_method,\n",
-    "            model=model,\n",
-    "            transforms=transforms,\n",
-    "            feature_columns=[\"image\"],\n",
-    "        )\n",
-    "    detector = Detector(\n",
-    "        \"sensitivity_test\",\n",
-    "        reductor=reductor,\n",
-    "        tester=TSTester(tester_method=\"ks\"),\n",
-    "        source_sample_size=50,\n",
-    "        target_sample_size=[10, 25, 50],\n",
-    "        num_runs=1,\n",
-    "    )\n",
-    "    result = detector.detect_shift(source_ds, target_ds)\n",
-    "    results[name] = result\n",
-    "plot_drift_experiment(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec20a728",
-   "metadata": {},
-   "source": [
-    "## Example 3. Sensitivity test experiment with models trained on different datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "40b5a90f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models = {\n",
-    "    \"MODEL: NIH\": \"densenet121-res224-nih\",\n",
-    "    \"MODEL: CHEXPERT\": \"densenet121-res224-chex\",\n",
-    "    \"MODEL: PADCHEST\": \"densenet121-res224-pc\",\n",
-    "}\n",
-    "results = {}\n",
-    "\n",
-    "for model_name, model in models.items():\n",
-    "    detector = Detector(\n",
-    "        \"sensitivity_test\",\n",
-    "        reductor=Reductor(\n",
-    "            dr_method=\"bbse-soft\",\n",
-    "            model=DenseNet(weights=model),\n",
-    "            transforms=transforms,\n",
-    "            feature_columns=[\"image\"],\n",
-    "        ),\n",
-    "        tester=TSTester(tester_method=\"ks\"),\n",
-    "        source_sample_size=50,\n",
-    "        target_sample_size=[10, 25, 50],\n",
-    "        num_runs=1,\n",
-    "    )\n",
-    "    results[model_name] = detector.detect_shift(source_ds, target_ds)\n",
-    "plot_drift_experiment(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e8ebcf1",
-   "metadata": {},
-   "source": [
-    "## Example 4. Sensitivity test experiment with different clinical shifts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9ba03fac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = DenseNet(weights=\"densenet121-res224-all\")\n",
-    "source_slice = None\n",
-    "target_slices = {\n",
-    "    \"SEX: MALE\": SliceSpec(spec_list=[{\"Patient Gender\": {\"value\": \"M\"}}]),\n",
-    "    \"SEX: FEMALE\": SliceSpec(spec_list=[{\"Patient Gender\": {\"value\": \"F\"}}]),\n",
-    "    \"AGE: 18-35\": SliceSpec(\n",
-    "        spec_list=[{\"Patient Age\": {\"min_value\": 18, \"max_value\": 35}}],\n",
-    "    ),\n",
-    "    \"AGE: 35-65\": SliceSpec(\n",
-    "        spec_list=[{\"Patient Age\": {\"min_value\": 35, \"max_value\": 65}}],\n",
-    "    ),\n",
-    "}\n",
-    "results = {}\n",
-    "\n",
-    "for name, target_slice in target_slices.items():\n",
-    "    source_slice = None\n",
-    "    shifter = ClinicalShiftApplicator(\n",
-    "        \"custom\",\n",
-    "        source=source_slice,\n",
-    "        target=target_slice,\n",
-    "    )\n",
-    "    ds_source, ds_target = shifter.apply_shift(nih_ds, num_proc=6)\n",
-    "\n",
-    "    detector = Detector(\n",
-    "        \"sensitivity_test\",\n",
-    "        reductor=Reductor(\n",
-    "            dr_method=\"bbse-soft\",\n",
-    "            model=model,\n",
-    "            transforms=transforms,\n",
-    "            feature_columns=[\"image\"],\n",
-    "        ),\n",
-    "        tester=TSTester(tester_method=\"ks\"),\n",
-    "        source_sample_size=50,\n",
-    "        target_sample_size=[10, 25, 50],\n",
-    "        num_runs=1,\n",
-    "    )\n",
-    "    results[name] = detector.detect_shift(ds_source, ds_target)\n",
-    "plot_drift_experiment(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7571bc9f",
-   "metadata": {},
-   "source": [
-    "## Example 5. Rolling window experiment with synthetic timestamps using biweekly window"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "77e4b383",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model = DenseNet(weights=\"densenet121-res224-all\")\n",
-    "detector = Detector(\n",
-    "    \"rolling_window_drift\",\n",
-    "    reductor=Reductor(\n",
-    "        dr_method=\"bbse-soft\",\n",
-    "        model=model,\n",
-    "        transforms=transforms,\n",
-    "        feature_columns=[\"image\"],\n",
-    "    ),\n",
-    "    tester=TSTester(tester_method=\"ks\"),\n",
-    "    source_sample_size=50,\n",
-    "    target_sample_size=10,\n",
-    "    timestamp_column=\"timestamp\",\n",
-    "    window_size=\"4W\",\n",
-    ")\n",
-    "\n",
-    "results = detector.detect_shift(source_ds, target_ds)\n",
-    "plot_drift_timeseries(results)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "nbsphinx": {
-   "execute": "never"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html
deleted file mode 100644
index 0051693f1..000000000
--- a/api/tutorials/synthea/los_prediction.html
+++ /dev/null
@@ -1,1523 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en">
-  <head><meta charset="utf-8"/>
-    <meta name="viewport" content="width=device-width,initial-scale=1"/>
-    <meta name="color-scheme" content="light dark">
-    <meta name="google-site-verification" content="i0qQRaR9OA3tSz_9tDocdcXGY27Ox_cy4FrvTHD2C_0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-
-    
-      <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Readmission Prediction" href="../diabetes_130/readmission_prediction.html" /><link rel="prev" title="Mortality Prediction" href="../mimiciv/mortality_prediction.html" />
-
-    <link rel="shortcut icon" href="../../_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
-
-    
-    <meta name="msvalidate.01" content="8D2A5032F006424CAF54C0DDCD16F666" />
-      
-        <title>Prolonged Length of Stay Prediction - cyclops documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=a746c00c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
-    <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
-    <link rel="stylesheet" type="text/css" href="../../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
-    <link rel="stylesheet" type="text/css" href="../../_static/nbsphinx-code-cells.css" />
-    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../../_static/css/cyclops.css?v=9f20b50b" />
-    
-    
-
-
-<style>
-  body {
-    --color-code-background: #f8f8f8;
-  --color-code-foreground: black;
-  --color-card-background: #5e88f2;
-  
-  }
-  @media not print {
-    body[data-theme="dark"] {
-      --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-    }
-    @media (prefers-color-scheme: dark) {
-      body:not([data-theme="light"]) {
-        --color-code-background: #202020;
-  --color-code-foreground: #d0d0d0;
-  --color-brand-primary: #faad1a;
-  --color-brand-content: #eb088a;
-  --color-foreground-secondary: #52c7de;
-  --color-card-background: #3b0e28;
-  
-      }
-    }
-  }
-</style><script src="../../_static/documentation_options.js?v=5929fcd5"></script>
-    <script src="../../_static/doctools.js?v=888ff710"></script>
-    <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
-    <script src="../../_static/scripts/furo.js?v=32e29ea5"></script>
-    <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
-    <script src="../../_static/copybutton.js?v=ccdb6887"></script>
-    <script src="../../_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-    <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-    <script src="../../_static/require.min.js?v=53367eec"></script>
-    <script src="../../_static/custom.js?v=275847af"></script>
-    
-    <script src="https://kit.fontawesome.com/2c1f516901.js" crossorigin="anonymous"></script>
-
-  </head>
-  <body>
-    
-    <script>
-      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
-    </script>
-    
-
-<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
-  <symbol id="svg-toc" viewBox="0 0 24 24">
-    <title>Contents</title>
-    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
-      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
-    </svg>
-  </symbol>
-  <symbol id="svg-menu" viewBox="0 0 24 24">
-    <title>Menu</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
-      <line x1="3" y1="12" x2="21" y2="12"></line>
-      <line x1="3" y1="6" x2="21" y2="6"></line>
-      <line x1="3" y1="18" x2="21" y2="18"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
-    <title>Expand</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
-      <polyline points="9 18 15 12 9 6"></polyline>
-    </svg>
-  </symbol>
-  <symbol id="svg-sun" viewBox="0 0 24 24">
-    <title>Light mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
-      <circle cx="12" cy="12" r="5"></circle>
-      <line x1="12" y1="1" x2="12" y2="3"></line>
-      <line x1="12" y1="21" x2="12" y2="23"></line>
-      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
-      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
-      <line x1="1" y1="12" x2="3" y2="12"></line>
-      <line x1="21" y1="12" x2="23" y2="12"></line>
-      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
-      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
-    </svg>
-  </symbol>
-  <symbol id="svg-moon" viewBox="0 0 24 24">
-    <title>Dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
-      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
-    </svg>
-  </symbol>
-  <symbol id="svg-sun-half" viewBox="0 0 24 24">
-    <title>Auto light/dark mode</title>
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
-      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
-      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-      <circle cx="12" cy="12" r="9" />
-      <path d="M13 12h5" />
-      <path d="M13 15h4" />
-      <path d="M13 18h1" />
-      <path d="M13 9h4" />
-      <path d="M13 6h1" />
-    </svg>
-  </symbol>
-</svg>
-
-<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
-<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
-<label class="overlay sidebar-overlay" for="__navigation">
-  <div class="visually-hidden">Hide navigation sidebar</div>
-</label>
-<label class="overlay toc-overlay" for="__toc">
-  <div class="visually-hidden">Hide table of contents sidebar</div>
-</label>
-
-
-
-<div class="page">
-  <header class="mobile-header">
-    <div class="header-left">
-      <label class="nav-overlay-icon" for="__navigation">
-        <div class="visually-hidden">Toggle site navigation sidebar</div>
-        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
-      </label>
-    </div>
-    <div class="header-center">
-      <a href="../../index.html"><div class="brand">cyclops  documentation</div></a>
-    </div>
-    <div class="header-right">
-      <div class="theme-toggle-container theme-toggle-header">
-        <button class="theme-toggle">
-          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-        </button>
-      </div>
-      <label class="toc-overlay-icon toc-header-icon" for="__toc">
-        <div class="visually-hidden">Toggle table of contents sidebar</div>
-        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-      </label>
-    </div>
-  </header>
-  <aside class="sidebar-drawer">
-    <div class="sidebar-container">
-      
-      <div class="sidebar-sticky"><a class="sidebar-brand" href="../../index.html">
-  
-  <div class="sidebar-logo-container">
-    <img class="sidebar-logo" src="../../_static/cyclops_logo-dark.png" alt="Logo"/>
-  </div>
-  
-  
-</a><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
-  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
-  <input type="hidden" name="check_keywords" value="yes">
-  <input type="hidden" name="area" value="default">
-</form>
-<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <ul class="current">
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../examples/report.html">Report Generation for Heart Failure Prediction</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../monitoring.html">Monitoring</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current has-children"><a class="reference internal" href="../../tutorials.html">Tutorials</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l2 current has-children"><a class="reference internal" href="../../tutorials_use_cases.html">Example use cases</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="../kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../developing.html">🧑🏿‍💻 Developing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing to cyclops</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../../api.html">API Reference</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of API Reference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.data.html">cyclops.data</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of cyclops.data</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.html">cyclops.data.slicer</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of cyclops.data.slicer</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.compound_filter.html">cyclops.data.slicer.compound_filter</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_datetime.html">cyclops.data.slicer.filter_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_non_null.html">cyclops.data.slicer.filter_non_null</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_range.html">cyclops.data.slicer.filter_range</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.html">cyclops.data.slicer.filter_string_contains</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.filter_value.html">cyclops.data.slicer.filter_value</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.is_datetime.html">cyclops.data.slicer.is_datetime</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.overall.html">cyclops.data.slicer.overall</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.slicer.SliceSpec.html">cyclops.data.slicer.SliceSpec</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.html">cyclops.data.aggregate</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of cyclops.data.aggregate</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.tabular_as_aggregated.html">cyclops.data.aggregate.tabular_as_aggregated</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.timestamp_ffill_agg.html">cyclops.data.aggregate.timestamp_ffill_agg</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.aggregate.Aggregator.html">cyclops.data.aggregate.Aggregator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.html">cyclops.data.features.medical_image</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of cyclops.data.features.medical_image</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.html">cyclops.data.features.medical_image.MedicalImage</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.tasks.html">cyclops.tasks</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of cyclops.tasks</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.html">cyclops.tasks.classification</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of cyclops.tasks.classification</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.BinaryTabularClassificationTask.html">cyclops.tasks.classification.BinaryTabularClassificationTask</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.tasks.classification.MultilabelImageClassificationTask.html">cyclops.tasks.classification.MultilabelImageClassificationTask</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/evaluator.html">Evaluator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../reference/api/fairness_evaluator.html">Fairness Evaluator</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/metrics.html">Evaluation Metrics</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of Evaluation Metrics</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/accuracy.html">Accuracy</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/auroc.html">AUROC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/average_precision.html">Average Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f1score.html">F1-Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/f_beta.html">F-beta Score</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision.html">Precision</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/precision_recall_curve.html">PrecisionRecallCurve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/recall.html">Recall</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/roc.html">ROC Curve</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/sensitivity.html">Sensitivity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/specificity.html">Specificity</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../reference/api/metrics/stat_scores.html">StatScores</a></li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.monitor.html">cyclops.monitor</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of cyclops.monitor</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.html">cyclops.monitor.clinical_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of cyclops.monitor.clinical_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.html">cyclops.monitor.clinical_applicator.ClinicalShiftApplicator</a></li>
-</ul>
-</li>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.html">cyclops.monitor.synthetic_applicator</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of cyclops.monitor.synthetic_applicator</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.html">cyclops.monitor.synthetic_applicator.binary_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.html">cyclops.monitor.synthetic_applicator.feature_association_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.html">cyclops.monitor.synthetic_applicator.feature_swap_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.html">cyclops.monitor.synthetic_applicator.gaussian_noise_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.html">cyclops.monitor.synthetic_applicator.knockout_shift</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.html">cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator</a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="../../reference/api/cyclops.report.html">cyclops.report</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of cyclops.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3 has-children"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.html">cyclops.report.report</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of cyclops.report.report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../reference/api/_autosummary/cyclops.report.report.ModelCardReport.html">cyclops.report.report.ModelCardReport</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-</div>
-</div>
-
-      </div>
-      
-    </div>
-  </aside>
-  <div class="main">
-    <div class="content">
-      <div class="article-container">
-        <a href="#" class="back-to-top muted-link">
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
-            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
-          </svg>
-          <span>Back to top</span>
-        </a>
-        <div class="content-icon-container">
-          
-<div class="theme-toggle-container theme-toggle-content">
-            <button class="theme-toggle">
-              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-            </button>
-          </div>
-          <label class="toc-overlay-icon toc-content-icon" for="__toc">
-            <div class="visually-hidden">Toggle table of contents sidebar</div>
-            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-          </label>
-        </div>
-        <article role="main">
-          <section id="Prolonged-Length-of-Stay-Prediction">
-<h1>Prolonged Length of Stay Prediction<a class="headerlink" href="#Prolonged-Length-of-Stay-Prediction" title="Link to this heading">#</a></h1>
-<p>This notebook showcases length of stay prediction on the <a class="reference external" href="https://github.com/synthetichealth/synthea">Synthea</a> dataset using CyclOps. The task is formulated as a binary classification task, where we predict the probability that a patient will stay 7 days or longer.</p>
-<p>To generate the synthetic patient data:</p>
-<ol class="arabic simple">
-<li><p>Generate synthea data using their releases. We used <a class="reference external" href="https://github.com/synthetichealth/synthea/releases/tag/v3.0.0">v3.0.0</a>.</p></li>
-<li><p>Follow instructions provided in <a class="reference external" href="https://github.com/OHDSI/ETL-Synthea">ETL-Synthea</a> to load the CSV data into a postgres database.</p></li>
-</ol>
-<section id="Import-Libraries">
-<h2>Import Libraries<a class="headerlink" href="#Import-Libraries" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;Prolonged Length of Stay Prediction.&quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">shutil</span>
-<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">date</span>
-
-<span class="kn">import</span> <span class="nn">cycquery.ops</span> <span class="k">as</span> <span class="nn">qo</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
-<span class="kn">import</span> <span class="nn">plotly.graph_objects</span> <span class="k">as</span> <span class="nn">go</span>
-<span class="kn">from</span> <span class="nn">cycquery</span> <span class="kn">import</span> <span class="n">DatasetQuerier</span>
-<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">Dataset</span>
-<span class="kn">from</span> <span class="nn">datasets.features</span> <span class="kn">import</span> <span class="n">ClassLabel</span>
-<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
-<span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
-<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span><span class="p">,</span> <span class="n">OneHotEncoder</span>
-
-<span class="kn">from</span> <span class="nn">cyclops.data.df.feature</span> <span class="kn">import</span> <span class="n">TabularFeatures</span>
-<span class="kn">from</span> <span class="nn">cyclops.data.slicer</span> <span class="kn">import</span> <span class="n">SliceSpec</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.fairness</span> <span class="kn">import</span> <span class="n">FairnessConfig</span>  <span class="c1"># noqa: E402</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics</span> <span class="kn">import</span> <span class="n">create_metric</span>
-<span class="kn">from</span> <span class="nn">cyclops.evaluate.metrics.experimental.metric_dict</span> <span class="kn">import</span> <span class="n">MetricDict</span>
-<span class="kn">from</span> <span class="nn">cyclops.models.catalog</span> <span class="kn">import</span> <span class="n">create_model</span>
-<span class="kn">from</span> <span class="nn">cyclops.report</span> <span class="kn">import</span> <span class="n">ModelCardReport</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.plot.classification</span> <span class="kn">import</span> <span class="n">ClassificationPlotter</span>
-<span class="kn">from</span> <span class="nn">cyclops.report.utils</span> <span class="kn">import</span> <span class="n">flatten_results_dict</span>
-<span class="kn">from</span> <span class="nn">cyclops.tasks</span> <span class="kn">import</span> <span class="n">BinaryTabularClassificationTask</span>
-</pre></div>
-</div>
-</div>
-<p>CyclOps offers a package for documentation of the model through a model report. The <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> class is used to populate and generate the model report as an HTML file. The model report has the following sections:</p>
-<ul class="simple">
-<li><p>Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).</p></li>
-<li><p>Datasets: High level statistics of the training data, including changes in distribution over time.</p></li>
-<li><p>Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.</p></li>
-<li><p>Fairness Analysis: This section contains the fairness metrics of the model.</p></li>
-<li><p>Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.</p></li>
-<li><p>Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.</p></li>
-<li><p>Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.</p></li>
-</ul>
-<p>We will use this to document the model development process as we go along and generate the model report at the end.</p>
-<p><code class="docutils literal notranslate"><span class="pre">The</span> <span class="pre">model</span> <span class="pre">report</span> <span class="pre">tool</span> <span class="pre">is</span> <span class="pre">a</span> <span class="pre">work</span> <span class="pre">in</span> <span class="pre">progress</span> <span class="pre">and</span> <span class="pre">is</span> <span class="pre">subject</span> <span class="pre">to</span> <span class="pre">change.</span></code></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span> <span class="o">=</span> <span class="n">ModelCardReport</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Constants">
-<h2>Constants<a class="headerlink" href="#Constants" title="Link to this heading">#</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">NAN_THRESHOLD</span> <span class="o">=</span> <span class="mf">0.25</span>
-<span class="n">NUM_DAYS</span> <span class="o">=</span> <span class="mi">7</span>
-<span class="n">TRAIN_SIZE</span> <span class="o">=</span> <span class="mf">0.8</span>
-<span class="n">RANDOM_SEED</span> <span class="o">=</span> <span class="mi">85</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Data-Querying">
-<h2>Data Querying<a class="headerlink" href="#Data-Querying" title="Link to this heading">#</a></h2>
-<section id="Compute-length-of-stay-(labels)">
-<h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-of-stay-(labels)" title="Link to this heading">#</a></h3>
-<ol class="arabic simple">
-<li><p>Get encounters, compute length of stay.</p></li>
-<li><p>Filter out encounters less than 2 days and greater than 20 days.</p></li>
-</ol>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">querier</span> <span class="o">=</span> <span class="n">DatasetQuerier</span><span class="p">(</span>
-    <span class="n">dbms</span><span class="o">=</span><span class="s2">&quot;postgresql&quot;</span><span class="p">,</span>
-    <span class="n">port</span><span class="o">=</span><span class="mi">5432</span><span class="p">,</span>
-    <span class="n">host</span><span class="o">=</span><span class="s2">&quot;localhost&quot;</span><span class="p">,</span>
-    <span class="n">database</span><span class="o">=</span><span class="s2">&quot;synthea_demo&quot;</span><span class="p">,</span>
-    <span class="n">user</span><span class="o">=</span><span class="s2">&quot;postgres&quot;</span><span class="p">,</span>
-    <span class="n">password</span><span class="o">=</span><span class="s2">&quot;pwd&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">get_encounters</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get encounters data.&quot;&quot;&quot;</span>
-    <span class="n">patients</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">native</span><span class="o">.</span><span class="n">patients</span><span class="p">()</span>
-    <span class="n">ops</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">Rename</span><span class="p">({</span><span class="s2">&quot;id&quot;</span><span class="p">:</span> <span class="s2">&quot;patient_id&quot;</span><span class="p">}),</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">Keep</span><span class="p">([</span><span class="s2">&quot;patient_id&quot;</span><span class="p">,</span> <span class="s2">&quot;birthdate&quot;</span><span class="p">,</span> <span class="s2">&quot;gender&quot;</span><span class="p">,</span> <span class="s2">&quot;race&quot;</span><span class="p">,</span> <span class="s2">&quot;ethnicity&quot;</span><span class="p">]),</span>
-    <span class="p">)</span>
-    <span class="n">patients</span> <span class="o">=</span> <span class="n">patients</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">ops</span><span class="p">)</span>
-    <span class="n">encounters</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">native</span><span class="o">.</span><span class="n">encounters</span><span class="p">()</span>
-    <span class="n">patient_encounters</span> <span class="o">=</span> <span class="n">encounters</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-        <span class="n">patients</span><span class="p">,</span>
-        <span class="n">on</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;patient&quot;</span><span class="p">,</span> <span class="s2">&quot;patient_id&quot;</span><span class="p">),</span>
-        <span class="n">isouter</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">ops</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">Rename</span><span class="p">({</span><span class="s2">&quot;id&quot;</span><span class="p">:</span> <span class="s2">&quot;encounter_id&quot;</span><span class="p">}),</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">ExtractTimestampComponent</span><span class="p">(</span><span class="s2">&quot;start&quot;</span><span class="p">,</span> <span class="s2">&quot;year&quot;</span><span class="p">,</span> <span class="s2">&quot;start_year&quot;</span><span class="p">),</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">ExtractTimestampComponent</span><span class="p">(</span><span class="s2">&quot;birthdate&quot;</span><span class="p">,</span> <span class="s2">&quot;year&quot;</span><span class="p">,</span> <span class="s2">&quot;birthdate_year&quot;</span><span class="p">),</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">AddColumn</span><span class="p">(</span>
-            <span class="s2">&quot;start_year&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;birthdate_year&quot;</span><span class="p">,</span>
-            <span class="n">new_col_labels</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span>
-            <span class="n">negative</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="p">),</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">AddColumn</span><span class="p">(</span><span class="s2">&quot;stop&quot;</span><span class="p">,</span> <span class="s2">&quot;start&quot;</span><span class="p">,</span> <span class="n">new_col_labels</span><span class="o">=</span><span class="s2">&quot;los&quot;</span><span class="p">,</span> <span class="n">negative</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">ConditionGreaterThan</span><span class="p">(</span><span class="s2">&quot;los&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">ConditionLessThan</span><span class="p">(</span><span class="s2">&quot;los&quot;</span><span class="p">,</span> <span class="mi">21</span><span class="p">),</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">Keep</span><span class="p">(</span>
-            <span class="p">[</span>
-                <span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;los&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;age&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;gender&quot;</span><span class="p">,</span>
-            <span class="p">],</span>
-        <span class="p">),</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">patient_encounters</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">ops</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">get_observations</span><span class="p">(</span><span class="n">cohort</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get observations data.&quot;&quot;&quot;</span>
-    <span class="n">observations</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">native</span><span class="o">.</span><span class="n">observations</span><span class="p">()</span>
-    <span class="n">ops</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">ConditionIn</span><span class="p">(</span>
-            <span class="s2">&quot;category&quot;</span><span class="p">,</span>
-            <span class="p">[</span>
-                <span class="s2">&quot;laboratory&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;vital-signs&quot;</span><span class="p">,</span>
-            <span class="p">],</span>
-        <span class="p">),</span>
-        <span class="n">qo</span><span class="o">.</span><span class="n">ConditionEquals</span><span class="p">(</span><span class="s2">&quot;type&quot;</span><span class="p">,</span> <span class="s2">&quot;numeric&quot;</span><span class="p">),</span>
-    <span class="p">)</span>
-    <span class="n">observations</span> <span class="o">=</span> <span class="n">observations</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">ops</span><span class="p">)</span>
-    <span class="n">cohort</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-        <span class="n">observations</span><span class="p">,</span>
-        <span class="n">on</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span> <span class="s2">&quot;encounter&quot;</span><span class="p">),</span>
-        <span class="n">isouter</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">groupby_op</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">GroupByAggregate</span><span class="p">(</span>
-        <span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span>
-        <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">,</span> <span class="s2">&quot;n_obs&quot;</span><span class="p">)},</span>
-    <span class="p">)</span>
-    <span class="n">observations</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
-    <span class="n">observations_count</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">groupby_op</span><span class="p">)</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
-    <span class="n">observations_stats</span> <span class="o">=</span> <span class="n">observations</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span>
-        <span class="n">index</span><span class="o">=</span><span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span>
-        <span class="n">columns</span><span class="o">=</span><span class="s2">&quot;description&quot;</span><span class="p">,</span>
-        <span class="n">values</span><span class="o">=</span><span class="s2">&quot;value&quot;</span><span class="p">,</span>
-        <span class="n">aggfunc</span><span class="o">=</span><span class="s2">&quot;max&quot;</span><span class="p">,</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">add_prefix</span><span class="p">(</span><span class="s2">&quot;obs_&quot;</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="p">[</span><span class="n">observations_count</span><span class="p">,</span> <span class="n">observations_stats</span><span class="p">]</span>
-
-
-<span class="k">def</span> <span class="nf">get_medications</span><span class="p">(</span><span class="n">cohort</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get medications data.&quot;&quot;&quot;</span>
-    <span class="n">medications</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">native</span><span class="o">.</span><span class="n">medications</span><span class="p">()</span>
-    <span class="n">cohort</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-        <span class="n">medications</span><span class="p">,</span>
-        <span class="n">on</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span> <span class="s2">&quot;encounter&quot;</span><span class="p">),</span>
-    <span class="p">)</span>
-    <span class="n">groupby_op</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">GroupByAggregate</span><span class="p">(</span>
-        <span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span>
-        <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">,</span> <span class="s2">&quot;n_meds&quot;</span><span class="p">)},</span>
-    <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">cohort</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">groupby_op</span><span class="p">)</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
-
-
-<span class="k">def</span> <span class="nf">get_procedures</span><span class="p">(</span><span class="n">cohort</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get procedures data.&quot;&quot;&quot;</span>
-    <span class="n">procedures</span> <span class="o">=</span> <span class="n">querier</span><span class="o">.</span><span class="n">native</span><span class="o">.</span><span class="n">procedures</span><span class="p">()</span>
-    <span class="n">cohort</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-        <span class="n">procedures</span><span class="p">,</span>
-        <span class="n">on</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span> <span class="s2">&quot;encounter&quot;</span><span class="p">),</span>
-    <span class="p">)</span>
-    <span class="n">groupby_op</span> <span class="o">=</span> <span class="n">qo</span><span class="o">.</span><span class="n">GroupByAggregate</span><span class="p">(</span>
-        <span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span>
-        <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">,</span> <span class="s2">&quot;n_procedures&quot;</span><span class="p">)},</span>
-    <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">cohort</span><span class="o">.</span><span class="n">ops</span><span class="p">(</span><span class="n">groupby_op</span><span class="p">)</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
-
-
-<span class="k">def</span> <span class="nf">run_query</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Run query pipeline.&quot;&quot;&quot;</span>
-    <span class="n">cohort_query</span> <span class="o">=</span> <span class="n">get_encounters</span><span class="p">()</span>
-    <span class="n">to_merge</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">observations</span> <span class="o">=</span> <span class="n">get_observations</span><span class="p">(</span><span class="n">cohort_query</span><span class="p">)</span>
-    <span class="n">to_merge</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">observations</span><span class="p">)</span>
-    <span class="n">medications</span> <span class="o">=</span> <span class="n">get_medications</span><span class="p">(</span><span class="n">cohort_query</span><span class="p">)</span>
-    <span class="n">to_merge</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">medications</span><span class="p">)</span>
-    <span class="n">procedures</span> <span class="o">=</span> <span class="n">get_procedures</span><span class="p">(</span><span class="n">cohort_query</span><span class="p">)</span>
-    <span class="n">to_merge</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">procedures</span><span class="p">)</span>
-    <span class="n">cohort</span> <span class="o">=</span> <span class="n">cohort_query</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">to_merge_df</span> <span class="ow">in</span> <span class="n">to_merge</span><span class="p">:</span>
-        <span class="n">cohort</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span>
-            <span class="n">to_merge_df</span><span class="p">,</span>
-            <span class="n">on</span><span class="o">=</span><span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span>
-            <span class="n">how</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span>
-        <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">cohort</span>
-
-
-<span class="n">cohort</span> <span class="o">=</span> <span class="n">run_query</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="Data-Inspection-and-Preprocessing">
-<h2>Data Inspection and Preprocessing<a class="headerlink" href="#Data-Inspection-and-Preprocessing" title="Link to this heading">#</a></h2>
-<section id="Drop-NaNs-based-on-the-NAN_THRESHOLD">
-<h3>Drop NaNs based on the <code class="docutils literal notranslate"><span class="pre">NAN_THRESHOLD</span></code><a class="headerlink" href="#Drop-NaNs-based-on-the-NAN_THRESHOLD" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">null_counts</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()[</span><span class="n">cohort</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[</span><span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">null_counts</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">null_counts</span><span class="o">.</span><span class="n">values</span><span class="p">)])</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Number of Null Values per Column&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Columns&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Number of Null Values&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<p>We can use the log_plotly_figure method to add the figure to a section of the report. One can specify whether the figure should be interactive or not by setting the <code class="docutils literal notranslate"><span class="pre">interactive</span></code> parameter to <code class="docutils literal notranslate"><span class="pre">True</span></code> or <code class="docutils literal notranslate"><span class="pre">False</span></code> respectively. The default value is <code class="docutils literal notranslate"><span class="pre">True</span></code>. This also affects the final size of the report. If the figure is interactive, the size of the report will be larger than if the figure is not interactive.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Number of Null Values per Column&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">thresh_nan</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">NAN_THRESHOLD</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">cohort</span><span class="p">))</span>
-<span class="n">cohort</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">thresh</span><span class="o">=</span><span class="n">thresh_nan</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Length-of-stay-distribution">
-<h3>Length of stay distribution<a class="headerlink" href="#Length-of-stay-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">length_of_stay</span> <span class="o">=</span> <span class="n">cohort</span><span class="p">[</span><span class="s2">&quot;los&quot;</span><span class="p">]</span>
-<span class="n">length_of_stay_counts</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">length_of_stay</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-<span class="n">length_of_stay_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">length_of_stay</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-<span class="n">cohort</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">cohort</span><span class="p">[</span><span class="s2">&quot;los&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">NUM_DAYS</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[</span><span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">length_of_stay_keys</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">length_of_stay_counts</span><span class="p">)])</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Length of stay&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Days&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Number of encounters&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Length of stay distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Outcome-distribution">
-<h3>Outcome distribution<a class="headerlink" href="#Outcome-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cohort</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">cohort</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;int&quot;</span><span class="p">)</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">cohort</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span><span class="n">textinfo</span><span class="o">=</span><span class="s2">&quot;percent+label&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span><span class="n">title_text</span><span class="o">=</span><span class="s2">&quot;Outcome Distribution&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_traces</span><span class="p">(</span>
-    <span class="n">hovertemplate</span><span class="o">=</span><span class="s2">&quot;Outcome: %</span><span class="si">{label}</span><span class="s2">&lt;br&gt;Count: </span><span class="se">\</span>
-<span class="s2">    %</span><span class="si">{value}</span><span class="s2">&lt;br&gt;Percent: %</span><span class="si">{percent}</span><span class="s2">&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Outcome Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">class_counts</span> <span class="o">=</span> <span class="n">cohort</span><span class="p">[</span><span class="s2">&quot;outcome&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
-<span class="n">class_ratio</span> <span class="o">=</span> <span class="n">class_counts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">class_counts</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">class_ratio</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Gender-distribution">
-<h3>Gender distribution<a class="headerlink" href="#Gender-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">cohort</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="s2">&quot;gender&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Gender Distribution&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Gender Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Age-distribution">
-<h3>Age distribution<a class="headerlink" href="#Age-distribution" title="Link to this heading">#</a></h3>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">cohort</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">xaxis_title</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">,</span>
-    <span class="n">yaxis_title</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">,</span>
-    <span class="n">bargap</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Add the figure to the report</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Age Distribution&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;datasets&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Identifying-feature-types">
-<h3>Identifying feature types<a class="headerlink" href="#Identifying-feature-types" title="Link to this heading">#</a></h3>
-<p>Cyclops <code class="docutils literal notranslate"><span class="pre">TabularFeatures</span></code> class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">features_list</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;age&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;gender&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;n_obs&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;n_meds&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;n_procedures&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Albumin [Mass/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Bilirubin.total [Mass/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Body Weight&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Calcium [Mass/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Carbon dioxide  total [Moles/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Chloride [Moles/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Creatinine [Mass/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Diastolic Blood Pressure&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Erythrocyte distribution width [Ratio] by Automated count&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Erythrocytes [#/volume] in Blood by Automated count&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Ferritin [Mass/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Glomerular filtration rate/1.73 sq M.predicted&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Glucose [Mass/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Hematocrit [Volume Fraction] of Blood by Automated count&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Hemoglobin [Mass/volume] in Blood&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Leukocytes [#/volume] in Blood by Automated count&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_MCH [Entitic mass] by Automated count&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_MCHC [Mass/volume] by Automated count&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_MCV [Entitic volume] by Automated count&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Oxygen saturation in Arterial blood&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Platelets [#/volume] in Blood by Automated count&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Potassium [Moles/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Protein [Mass/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Sodium [Moles/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Systolic Blood Pressure&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;obs_Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method&quot;</span><span class="p">,</span>  <span class="c1"># noqa: E501</span>
-    <span class="s2">&quot;obs_Urea nitrogen [Mass/volume] in Serum or Plasma&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-<span class="n">features_list</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">features_list</span><span class="p">)</span>
-<span class="n">tab_features</span> <span class="o">=</span> <span class="n">TabularFeatures</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="n">cohort</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(),</span>
-    <span class="n">features</span><span class="o">=</span><span class="n">features_list</span><span class="p">,</span>
-    <span class="n">by</span><span class="o">=</span><span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span>
-    <span class="n">targets</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">tab_features</span><span class="o">.</span><span class="n">types</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Creating-data-preprocessors">
-<h3>Creating data preprocessors<a class="headerlink" href="#Creating-data-preprocessors" title="Link to this heading">#</a></h3>
-<p>We create a data preprocessor using sklearn’s ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numeric_transformer</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span>
-    <span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;imputer&quot;</span><span class="p">,</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)),</span> <span class="p">(</span><span class="s2">&quot;scaler&quot;</span><span class="p">,</span> <span class="n">MinMaxScaler</span><span class="p">())],</span>
-<span class="p">)</span>
-
-<span class="n">binary_transformer</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span>
-    <span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;imputer&quot;</span><span class="p">,</span> <span class="n">SimpleImputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;most_frequent&quot;</span><span class="p">))],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numeric_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">((</span><span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;numeric&quot;</span><span class="p">)))</span>
-<span class="n">numeric_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">cohort</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">numeric_features</span>
-<span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">numeric_features</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">binary_features</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">tab_features</span><span class="o">.</span><span class="n">features_by_type</span><span class="p">(</span><span class="s2">&quot;binary&quot;</span><span class="p">))</span>
-<span class="n">binary_features</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s2">&quot;outcome&quot;</span><span class="p">)</span>
-<span class="n">binary_indices</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">cohort</span><span class="p">[</span><span class="n">features_list</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">column</span><span class="p">)</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">binary_features</span>
-<span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">binary_features</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">preprocessor</span> <span class="o">=</span> <span class="n">ColumnTransformer</span><span class="p">(</span>
-    <span class="n">transformers</span><span class="o">=</span><span class="p">[</span>
-        <span class="p">(</span><span class="s2">&quot;num&quot;</span><span class="p">,</span> <span class="n">numeric_transformer</span><span class="p">,</span> <span class="n">numeric_indices</span><span class="p">),</span>
-        <span class="p">(</span><span class="s2">&quot;onehot&quot;</span><span class="p">,</span> <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">handle_unknown</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">),</span> <span class="n">binary_indices</span><span class="p">),</span>
-    <span class="p">],</span>
-    <span class="n">remainder</span><span class="o">=</span><span class="s2">&quot;passthrough&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="Creating-Hugging-Face-Dataset">
-<h2>Creating Hugging Face Dataset<a class="headerlink" href="#Creating-Hugging-Face-Dataset" title="Link to this heading">#</a></h2>
-<p>We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cohort</span> <span class="o">=</span> <span class="n">cohort</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;encounter_id&quot;</span><span class="p">,</span> <span class="s2">&quot;los&quot;</span><span class="p">])</span>
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">cohort</span><span class="p">)</span>
-<span class="n">dataset</span><span class="o">.</span><span class="n">cleanup_cache_files</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">cast_column</span><span class="p">(</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span> <span class="n">ClassLabel</span><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
-    <span class="n">train_size</span><span class="o">=</span><span class="n">TRAIN_SIZE</span><span class="p">,</span>
-    <span class="n">stratify_by_column</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span>
-    <span class="n">seed</span><span class="o">=</span><span class="n">RANDOM_SEED</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Model-Creation">
-<h2>Model Creation<a class="headerlink" href="#Model-Creation" title="Link to this heading">#</a></h2>
-<p>CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a XGBoost classifier to fit a logisitic regression model. The model configurations can be passed to <code class="docutils literal notranslate"><span class="pre">create_model</span></code> based on the parameters for XGBClassifier.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="s2">&quot;xgb_classifier&quot;</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">create_model</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Task-Creation">
-<h2>Task Creation<a class="headerlink" href="#Task-Creation" title="Link to this heading">#</a></h2>
-<p>We use Cyclops tasks to define our model’s task (in this case, BinaryTabularClassificationTask), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">los_task</span> <span class="o">=</span> <span class="n">BinaryTabularClassificationTask</span><span class="p">(</span>
-    <span class="p">{</span><span class="n">model_name</span><span class="p">:</span> <span class="n">model</span><span class="p">},</span>
-    <span class="n">task_features</span><span class="o">=</span><span class="n">features_list</span><span class="p">,</span>
-    <span class="n">task_target</span><span class="o">=</span><span class="s2">&quot;outcome&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">los_task</span><span class="o">.</span><span class="n">list_models</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Training">
-<h2>Training<a class="headerlink" href="#Training" title="Link to this heading">#</a></h2>
-<p>If <code class="docutils literal notranslate"><span class="pre">best_model_params</span></code> is passed to the <code class="docutils literal notranslate"><span class="pre">train</span></code> method, the best model will be selected after the hyperparameter search. The parameters in <code class="docutils literal notranslate"><span class="pre">best_model_params</span></code> indicate the values to create the parameters grid.</p>
-<p>Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">best_model_params</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;n_estimators&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">500</span><span class="p">],</span>
-    <span class="s2">&quot;learning_rate&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">],</span>
-    <span class="s2">&quot;max_depth&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
-    <span class="s2">&quot;reg_lambda&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span>
-    <span class="s2">&quot;colsample_bytree&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
-    <span class="s2">&quot;gamma&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span>
-    <span class="s2">&quot;method&quot;</span><span class="p">:</span> <span class="s2">&quot;random&quot;</span><span class="p">,</span>
-<span class="p">}</span>
-<span class="n">los_task</span><span class="o">.</span><span class="n">train</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">best_model_params</span><span class="o">=</span><span class="n">best_model_params</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_params</span> <span class="o">=</span> <span class="n">los_task</span><span class="o">.</span><span class="n">list_models_params</span><span class="p">()[</span><span class="n">model_name</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">model_params</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Log the model parameters to the report.</strong></p>
-<p>We can add model parameters to the model card using the <code class="docutils literal notranslate"><span class="pre">log_model_parameters</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_model_parameters</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="n">model_params</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Prediction">
-<h2>Prediction<a class="headerlink" href="#Prediction" title="Link to this heading">#</a></h2>
-<p>The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">los_task</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">proba</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">only_predictions</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_pred</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Evaluation">
-<h2>Evaluation<a class="headerlink" href="#Evaluation" title="Link to this heading">#</a></h2>
-<p>Evaluation is done using various evaluation metrics that provide different perspectives on the model’s predictive abilities i.e. standard performance metrics and fairness metrics.</p>
-<p>The standard performance metrics can be created using the <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code> object.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metric_names</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;binary_accuracy&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_precision&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_recall&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_f1_score&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_auroc&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_roc_curve&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_precision_recall_curve&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;binary_confusion_matrix&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric_name</span> <span class="ow">in</span> <span class="n">metric_names</span>
-<span class="p">]</span>
-<span class="n">metric_collection</span> <span class="o">=</span> <span class="n">MetricDict</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using <code class="docutils literal notranslate"><span class="pre">SliceSpec</span></code> objects.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">spec_list</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">20</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="p">{</span>
-            <span class="s2">&quot;min_value&quot;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
-            <span class="s2">&quot;max_value&quot;</span><span class="p">:</span> <span class="mi">80</span><span class="p">,</span>
-            <span class="s2">&quot;min_inclusive&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-            <span class="s2">&quot;max_inclusive&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-        <span class="p">},</span>
-    <span class="p">},</span>
-    <span class="p">{</span><span class="s2">&quot;gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">}},</span>
-    <span class="p">{</span><span class="s2">&quot;gender&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="s2">&quot;F&quot;</span><span class="p">}},</span>
-<span class="p">]</span>
-<span class="n">slice_spec</span> <span class="o">=</span> <span class="n">SliceSpec</span><span class="p">(</span><span class="n">spec_list</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>A <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code> can also be defined for the fairness metrics.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">specificity</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;binary_specificity&quot;</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">sensitivity</span> <span class="o">=</span> <span class="n">create_metric</span><span class="p">(</span><span class="n">metric_name</span><span class="o">=</span><span class="s2">&quot;binary_sensitivity&quot;</span><span class="p">,</span> <span class="n">experimental</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">fpr</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="o">-</span><span class="n">specificity</span> <span class="o">+</span> <span class="mi">1</span>
-<span class="p">)</span>  <span class="c1"># rsub is not supported due to limitations in the array API standard</span>
-<span class="n">fnr</span> <span class="o">=</span> <span class="o">-</span><span class="n">sensitivity</span> <span class="o">+</span> <span class="mi">1</span>
-<span class="n">ber</span> <span class="o">=</span> <span class="p">(</span><span class="n">fpr</span> <span class="o">+</span> <span class="n">fnr</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
-<span class="n">fairness_metric_collection</span> <span class="o">=</span> <span class="n">MetricDict</span><span class="p">(</span>
-    <span class="p">{</span>
-        <span class="s2">&quot;Sensitivity&quot;</span><span class="p">:</span> <span class="n">sensitivity</span><span class="p">,</span>
-        <span class="s2">&quot;Specificity&quot;</span><span class="p">:</span> <span class="n">specificity</span><span class="p">,</span>
-        <span class="s2">&quot;BER&quot;</span><span class="p">:</span> <span class="n">ber</span><span class="p">,</span>
-    <span class="p">},</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The FairnessConfig helps in setting up and evaluating the fairness of the model predictions.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fairness_config</span> <span class="o">=</span> <span class="n">FairnessConfig</span><span class="p">(</span>
-    <span class="n">metrics</span><span class="o">=</span><span class="n">fairness_metric_collection</span><span class="p">,</span>
-    <span class="n">dataset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># dataset is passed from the evaluator</span>
-    <span class="n">target_columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># target columns are passed from the evaluator</span>
-    <span class="n">groups</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;gender&quot;</span><span class="p">,</span> <span class="s2">&quot;age&quot;</span><span class="p">],</span>
-    <span class="n">group_bins</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">]},</span>
-    <span class="n">group_base_values</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="mi">40</span><span class="p">,</span> <span class="s2">&quot;gender&quot;</span><span class="p">:</span> <span class="s2">&quot;M&quot;</span><span class="p">},</span>
-    <span class="n">thresholds</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="p">,</span> <span class="n">dataset_with_preds</span> <span class="o">=</span> <span class="n">los_task</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
-    <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span>
-    <span class="n">metric_collection</span><span class="p">,</span>
-    <span class="n">model_names</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-    <span class="n">transforms</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">,</span>
-    <span class="n">prediction_column_prefix</span><span class="o">=</span><span class="s2">&quot;preds&quot;</span><span class="p">,</span>
-    <span class="n">slice_spec</span><span class="o">=</span><span class="n">slice_spec</span><span class="p">,</span>
-    <span class="n">batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">fairness_config</span><span class="o">=</span><span class="n">fairness_config</span><span class="p">,</span>
-    <span class="n">override_fairness_metrics</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p><strong>Log the performance metrics to the report.</strong></p>
-<p>We can add a performance metric to the model card using the <code class="docutils literal notranslate"><span class="pre">log_performance_metric</span></code> method, which expects a dictionary where the keys are in the following format: <code class="docutils literal notranslate"><span class="pre">slice_name/metric_name</span></code>. For instance, <code class="docutils literal notranslate"><span class="pre">overall/accuracy</span></code>.</p>
-<p>We first need to process the evaluation results to get the metrics in the right format.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;model_for_preds.</span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="n">results_flat</span> <span class="o">=</span> <span class="n">flatten_results_dict</span><span class="p">(</span>
-    <span class="n">results</span><span class="o">=</span><span class="n">results</span><span class="p">,</span>
-    <span class="n">remove_metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">],</span>
-    <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">results_flat</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">split</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)</span>  <span class="c1"># noqa: PLW2901</span>
-    <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;BinaryConfusionMatrix&quot;</span><span class="p">:</span>
-        <span class="k">continue</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;BinaryPrecision&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of predicted positive instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryRecall&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAccuracy&quot;</span><span class="p">:</span> <span class="s2">&quot;The proportion of all instances that are correctly predicted.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">:</span> <span class="s2">&quot;The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;BinaryF1Score&quot;</span><span class="p">:</span> <span class="s2">&quot;The harmonic mean of precision and recall.&quot;</span><span class="p">,</span>
-    <span class="p">}</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">log_quantitative_analysis</span><span class="p">(</span>
-        <span class="s2">&quot;performance&quot;</span><span class="p">,</span>
-        <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
-        <span class="n">value</span><span class="o">=</span><span class="n">metric</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-        <span class="n">description</span><span class="o">=</span><span class="n">descriptions</span><span class="p">[</span><span class="n">name</span><span class="p">],</span>
-        <span class="n">metric_slice</span><span class="o">=</span><span class="n">split</span><span class="p">,</span>
-        <span class="n">pass_fail_thresholds</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
-        <span class="n">pass_fail_threshold_fns</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">),</span>
-    <span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>We can also use the <code class="docutils literal notranslate"><span class="pre">ClassificationPlotter</span></code> to plot the performance metrics and the add the figure to the model card using the <code class="docutils literal notranslate"><span class="pre">log_plotly_figure</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plotter</span> <span class="o">=</span> <span class="n">ClassificationPlotter</span><span class="p">(</span><span class="n">task_type</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">])</span>
-<span class="n">plotter</span><span class="o">.</span><span class="n">set_template</span><span class="p">(</span><span class="s2">&quot;plotly_white&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># extracting the ROC curves and AUROC results for all the slices</span>
-<span class="n">roc_curves</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">aurocs</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="n">slice_results</span><span class="p">[</span><span class="s2">&quot;BinaryAUROC&quot;</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-<span class="n">roc_curves</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plot confusion matrix</span>
-<span class="n">confusion_matrix</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;overall&quot;</span><span class="p">][</span><span class="s2">&quot;BinaryConfusionMatrix&quot;</span><span class="p">]</span>
-<span class="n">conf_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">(</span>
-    <span class="n">confusion_matrix</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">conf_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Confusion Matrix&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">conf_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># plotting the ROC curves for all the slices</span>
-<span class="n">roc_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">roc_curve_comparison</span><span class="p">(</span><span class="n">roc_curves</span><span class="p">,</span> <span class="n">aurocs</span><span class="o">=</span><span class="n">aurocs</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">roc_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;ROC Curve for Female Patients&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">roc_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the overall classification metric values.</span>
-<span class="n">overall_performance</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-    <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;overall&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-    <span class="k">if</span> <span class="n">metric_name</span>
-    <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryROC&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryConfusionMatrix&quot;</span><span class="p">]</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the overall classification metric values.</span>
-<span class="n">overall_performance_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_value</span><span class="p">(</span>
-    <span class="n">overall_performance</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Overall Performance&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">overall_performance_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Overall Performance&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">overall_performance_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extracting the metric values for all the slices.</span>
-<span class="n">slice_metrics</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">slice_name</span><span class="p">:</span> <span class="p">{</span>
-        <span class="n">metric_name</span><span class="p">:</span> <span class="n">metric_value</span>
-        <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">slice_results</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">metric_name</span>
-        <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;BinaryROCCurve&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryPrecisionRecallCurve&quot;</span><span class="p">,</span> <span class="s2">&quot;BinaryConfusionMatrix&quot;</span><span class="p">]</span>
-    <span class="p">}</span>
-    <span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">results</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the metric values for all the slices.</span>
-<span class="n">slice_metrics_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_comparison_bar</span><span class="p">(</span><span class="n">slice_metrics</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">slice_metrics_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Slice Metric Comparison&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;quantitative analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">slice_metrics_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Reformatting the fairness metrics</span>
-<span class="n">fairness_results</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;fairness&quot;</span><span class="p">])</span>
-<span class="n">fairness_metrics</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="c1"># remove the group size from the fairness results and add it to the slice name</span>
-<span class="k">for</span> <span class="n">slice_name</span><span class="p">,</span> <span class="n">slice_results</span> <span class="ow">in</span> <span class="n">fairness_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">group_size</span> <span class="o">=</span> <span class="n">slice_results</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;Group Size&quot;</span><span class="p">)</span>
-    <span class="n">fairness_metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">slice_name</span><span class="si">}</span><span class="s2"> (Size=</span><span class="si">{</span><span class="n">group_size</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_results</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plotting the fairness metrics</span>
-<span class="n">fairness_plot</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">metrics_comparison_scatter</span><span class="p">(</span>
-    <span class="n">fairness_metrics</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Fairness Metrics&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_plotly_figure</span><span class="p">(</span>
-    <span class="n">fig</span><span class="o">=</span><span class="n">fairness_plot</span><span class="p">,</span>
-    <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Fairness Metrics&quot;</span><span class="p">,</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;fairness analysis&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">fairness_plot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Report-Generation">
-<h2>Report Generation<a class="headerlink" href="#Report-Generation" title="Link to this heading">#</a></h2>
-<p>Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.</p>
-<p>Let’s start with populating the model details section, which includes the following fields by default: - description: A high-level description of the model and its usage for a general audience. - version: The version of the model. - owners: The individuals or organizations that own the model. - license: The license under which the model is made available. - citation: The citation for the model. - references: Links to resources that are relevant to the model. - path: The path to where the model
-is stored. - regulatory_requirements: The regulatory requirements that are relevant to the model.</p>
-<p>We can add additional fields to the model details section by passing a dictionary to the <code class="docutils literal notranslate"><span class="pre">log_from_dict</span></code> method and specifying the section name as <code class="docutils literal notranslate"><span class="pre">model_details</span></code>. You can also use the <code class="docutils literal notranslate"><span class="pre">log_descriptor</span></code> method to add a new field object with a <code class="docutils literal notranslate"><span class="pre">description</span></code> attribute to any section of the model card.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;Prolonged Length of Stay Prediction Model&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;The model was trained on the Synthea synthetic dataset </span><span class="se">\</span>
-<span class="s2">            to predict prolonged stay in the hospital.&quot;</span><span class="p">,</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;model_details&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_version</span><span class="p">(</span>
-    <span class="n">version_str</span><span class="o">=</span><span class="s2">&quot;0.0.1&quot;</span><span class="p">,</span>
-    <span class="n">date</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">date</span><span class="o">.</span><span class="n">today</span><span class="p">()),</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Initial Release&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_owner</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;CyclOps Team&quot;</span><span class="p">,</span>
-    <span class="n">contact</span><span class="o">=</span><span class="s2">&quot;vectorinstitute.github.io/cyclops/&quot;</span><span class="p">,</span>
-    <span class="n">email</span><span class="o">=</span><span class="s2">&quot;cyclops@vectorinstitute.ai&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_license</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">&quot;Apache-2.0&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_reference</span><span class="p">(</span>
-    <span class="n">link</span><span class="o">=</span><span class="s2">&quot;https://xgboost.readthedocs.io/en/stable/python/python_api.html&quot;</span><span class="p">,</span>  <span class="c1"># noqa: E501</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Next, let’s populate the considerations section, which includes the following fields by default: - users: The intended users of the model. - use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases. - fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms. - ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This
-can be populated using the <code class="docutils literal notranslate"><span class="pre">log_risk</span></code> method.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report</span><span class="o">.</span><span class="n">log_from_dict</span><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">&quot;users&quot;</span><span class="p">:</span> <span class="p">[</span>
-            <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;Hospitals&quot;</span><span class="p">},</span>
-            <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="s2">&quot;Clinicians&quot;</span><span class="p">},</span>
-        <span class="p">],</span>
-    <span class="p">},</span>
-    <span class="n">section_name</span><span class="o">=</span><span class="s2">&quot;considerations&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_user</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;ML Engineers&quot;</span><span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_use_case</span><span class="p">(</span>
-    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Predicting prolonged length of stay&quot;</span><span class="p">,</span>
-    <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;primary&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_fairness_assessment</span><span class="p">(</span>
-    <span class="n">affected_group</span><span class="o">=</span><span class="s2">&quot;sex, age&quot;</span><span class="p">,</span>
-    <span class="n">benefit</span><span class="o">=</span><span class="s2">&quot;Improved health outcomes for patients.&quot;</span><span class="p">,</span>
-    <span class="n">harm</span><span class="o">=</span><span class="s2">&quot;Biased predictions for patients in certain groups (e.g. older patients) </span><span class="se">\</span>
-<span class="s2">        may lead to worse health outcomes.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;We will monitor the performance of the model on these groups </span><span class="se">\</span>
-<span class="s2">        and retrain the model if the performance drops below a certain threshold.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">report</span><span class="o">.</span><span class="n">log_risk</span><span class="p">(</span>
-    <span class="n">risk</span><span class="o">=</span><span class="s2">&quot;The model may be used to make decisions that affect the health of patients.&quot;</span><span class="p">,</span>
-    <span class="n">mitigation_strategy</span><span class="o">=</span><span class="s2">&quot;The model should be continuously monitored for performance </span><span class="se">\</span>
-<span class="s2">        and retrained if the performance drops below a certain threshold.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Once the model card is populated, you can generate the report using the <code class="docutils literal notranslate"><span class="pre">export</span></code> method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named <code class="docutils literal notranslate"><span class="pre">cyclops_reports</span></code> in the current working directory. You can change the path by passing a <code class="docutils literal notranslate"><span class="pre">output_dir</span></code> argument when instantiating the <code class="docutils literal notranslate"><span class="pre">ModelCardReport</span></code> class.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">synthetic_timestamps</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;2021-09-01&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;2021-10-01&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;2021-11-01&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;2021-12-01&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;2022-01-01&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-<span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">overview</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="n">report_path</span> <span class="o">=</span> <span class="n">report</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
-    <span class="n">output_filename</span><span class="o">=</span><span class="s2">&quot;length_of_stay_report_periodic.html&quot;</span><span class="p">,</span>
-    <span class="n">synthetic_timestamp</span><span class="o">=</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-<span class="p">)</span>
-<span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
-    <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">overview</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">report</span><span class="o">.</span><span class="n">_model_card</span><span class="o">.</span><span class="n">quantitative_analysis</span><span class="o">.</span><span class="n">performance_metrics</span><span class="p">:</span>
-        <span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span>
-            <span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span>
-            <span class="mi">0</span><span class="p">,</span>
-            <span class="mi">1</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">metric</span><span class="o">.</span><span class="n">tests</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">passed</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">metric</span><span class="o">.</span><span class="n">value</span> <span class="o">&gt;=</span> <span class="mf">0.7</span><span class="p">)</span>
-    <span class="n">report_path</span> <span class="o">=</span> <span class="n">report</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
-        <span class="n">output_filename</span><span class="o">=</span><span class="s2">&quot;length_of_stay_report_periodic.html&quot;</span><span class="p">,</span>
-        <span class="n">synthetic_timestamp</span><span class="o">=</span><span class="n">synthetic_timestamps</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span>
-    <span class="p">)</span>
-    <span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
-<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="s2">&quot;./cyclops_report&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>You can view the generated HTML <a class="reference external" href="./length_of_stay_report_periodic.html">report</a>.</p>
-</section>
-</section>
-
-        </article>
-      </div>
-      <footer>
-        
-        <div class="related-pages">
-          <a class="next-page" href="../diabetes_130/readmission_prediction.html">
-              <div class="page-info">
-                <div class="context">
-                  <span>Next</span>
-                </div>
-                <div class="title">Readmission Prediction</div>
-              </div>
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-            </a>
-          <a class="prev-page" href="../mimiciv/mortality_prediction.html">
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-              <div class="page-info">
-                <div class="context">
-                  <span>Previous</span>
-                </div>
-                
-                <div class="title">Mortality Prediction</div>
-                
-              </div>
-            </a>
-        </div>
-        <div class="bottom-of-page">
-          <div class="left-details">
-            <div class="copyright">
-                Copyright &#169; 2024, Vector AI Engineering
-            </div>
-            <!--
-            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-            
-            <a href="https://github.com/pradyunsg/furo">Furo</a>
-            
-            -->
-          </div>
-          <div class="right-details">
-            <div class="icons">
-              <a class="muted-link " href="https://github.com/VectorInstitute/cyclops" aria-label="GitHub">
-                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
-                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
-                </svg>
-            </a>
-              
-            </div>
-          </div>
-        </div>
-        <div class="footer-container" style="text-align: center;">
-          <div class="footer-small-text">
-              <br> All <a href="https://github.com/VectorInstitute/cyclops">source code for cyclops</a> is freely available under the terms of an <a href="https://opensource.org/licenses/Apache-2.0">Apache-2.0</a> license.
-          </div>
-
-          <div class="footer-small-text">
-            This work is made possible due to the data obtained from the <a href="https://www.geminimedicine.ca/">GEMINI study</a>.
-          </div>
-
-          <div class="logo">
-            <a class="logo" href="https://vectorinstitute.ai/">
-              <span class="logo"></span>
-              <img alt="Logo of the Vector Institute" style="width:90px; max-width:90px;" src="../../_static/logos/vector_logo.png" />
-            </a>
-            <a class="logo" href="https://www.geminimedicine.ca/">
-              <span class="logo"></span>
-              <img alt="Logo of GEMINI" style="width:90px; max-width:90px;" src="../../_static/logos/gemini_logo.png" />
-            </a>
-          </div>
-        </div>
-        
-      </footer>
-    </div>
-    <aside class="toc-drawer">
-      
-      
-      <div class="toc-sticky toc-scroll">
-        <div class="toc-title-container">
-          <span class="toc-title">
-            On this page
-          </span>
-        </div>
-        <div class="toc-tree-container">
-          <div class="toc-tree">
-            <ul>
-<li><a class="reference internal" href="#">Prolonged Length of Stay Prediction</a><ul>
-<li><a class="reference internal" href="#Import-Libraries">Import Libraries</a></li>
-<li><a class="reference internal" href="#Constants">Constants</a></li>
-<li><a class="reference internal" href="#Data-Querying">Data Querying</a><ul>
-<li><a class="reference internal" href="#Compute-length-of-stay-(labels)">Compute length of stay (labels)</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#Data-Inspection-and-Preprocessing">Data Inspection and Preprocessing</a><ul>
-<li><a class="reference internal" href="#Drop-NaNs-based-on-the-NAN_THRESHOLD">Drop NaNs based on the <code class="docutils literal notranslate"><span class="pre">NAN_THRESHOLD</span></code></a></li>
-<li><a class="reference internal" href="#Length-of-stay-distribution">Length of stay distribution</a></li>
-<li><a class="reference internal" href="#Outcome-distribution">Outcome distribution</a></li>
-<li><a class="reference internal" href="#Gender-distribution">Gender distribution</a></li>
-<li><a class="reference internal" href="#Age-distribution">Age distribution</a></li>
-<li><a class="reference internal" href="#Identifying-feature-types">Identifying feature types</a></li>
-<li><a class="reference internal" href="#Creating-data-preprocessors">Creating data preprocessors</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#Creating-Hugging-Face-Dataset">Creating Hugging Face Dataset</a></li>
-<li><a class="reference internal" href="#Model-Creation">Model Creation</a></li>
-<li><a class="reference internal" href="#Task-Creation">Task Creation</a></li>
-<li><a class="reference internal" href="#Training">Training</a></li>
-<li><a class="reference internal" href="#Prediction">Prediction</a></li>
-<li><a class="reference internal" href="#Evaluation">Evaluation</a></li>
-<li><a class="reference internal" href="#Report-Generation">Report Generation</a></li>
-</ul>
-</li>
-</ul>
-
-          </div>
-        </div>
-      </div>
-      
-      
-    </aside>
-  </div>
-</div></body>
-</html>
\ No newline at end of file
diff --git a/api/tutorials/synthea/los_prediction.ipynb b/api/tutorials/synthea/los_prediction.ipynb
deleted file mode 100644
index 53d4e4304..000000000
--- a/api/tutorials/synthea/los_prediction.ipynb
+++ /dev/null
@@ -1,1506 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e337389b-1cfe-4796-a846-b4e1ba5690d6",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Prolonged Length of Stay Prediction\n",
-    "\n",
-    "This notebook showcases length of stay prediction on the [Synthea](https://github.com/synthetichealth/synthea) dataset using CyclOps. The task is formulated as a binary classification task, where we predict the probability that a patient will stay 7 days or longer.\n",
-    "\n",
-    "To generate the synthetic patient data:\n",
-    "\n",
-    "1. Generate synthea data using their releases. We used [v3.0.0](https://github.com/synthetichealth/synthea/releases/tag/v3.0.0).\n",
-    "2. Follow instructions provided in [ETL-Synthea](https://github.com/OHDSI/ETL-Synthea) to load the CSV data into a postgres database."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12c18656-7f16-4230-85d0-944563d6a13e",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Import Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53009e6b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"Prolonged Length of Stay Prediction.\"\"\"\n",
-    "\n",
-    "import copy\n",
-    "import shutil\n",
-    "from datetime import date\n",
-    "\n",
-    "import cycquery.ops as qo\n",
-    "import numpy as np\n",
-    "import plotly.express as px\n",
-    "import plotly.graph_objects as go\n",
-    "from cycquery import DatasetQuerier\n",
-    "from datasets import Dataset\n",
-    "from datasets.features import ClassLabel\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
-    "\n",
-    "from cyclops.data.df.feature import TabularFeatures\n",
-    "from cyclops.data.slicer import SliceSpec\n",
-    "from cyclops.evaluate.fairness import FairnessConfig  # noqa: E402\n",
-    "from cyclops.evaluate.metrics import create_metric\n",
-    "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
-    "from cyclops.models.catalog import create_model\n",
-    "from cyclops.report import ModelCardReport\n",
-    "from cyclops.report.plot.classification import ClassificationPlotter\n",
-    "from cyclops.report.utils import flatten_results_dict\n",
-    "from cyclops.tasks import BinaryTabularClassificationTask"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0c35352-ccef-47c9-8d1a-3062c62adb97",
-   "metadata": {},
-   "source": [
-    "CyclOps offers a package for documentation of the model through a model report. The `ModelCardReport` class is used to populate and generate the model report as an HTML file. The model report has the following sections:\n",
-    "\n",
-    "- Overview: Provides a high level overview of how the model is doing (a quick glance of important metrics), and how it is doing over time (performance over several metrics and subgroups over time).\n",
-    "- Datasets: High level statistics of the training data, including changes in distribution over time.\n",
-    "- Quantitative Analysis: This section contains additional detailed performance metrics of the model for different sets of the data and subpopulations.\n",
-    "- Fairness Analysis: This section contains the fairness metrics of the model.\n",
-    "- Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc.\n",
-    "- Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc.\n",
-    "- Considerations: This section contains descriptions of the considerations involved in developing and using the model such as the intended use, limitations, etc.\n",
-    "\n",
-    "We will use this to document the model development process as we go along and generate the model report at the end.\n",
-    "\n",
-    "`The model report tool is a work in progress and is subject to change.`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "afae58a8-5708-4e05-8695-25ba3ce1a71f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "report = ModelCardReport()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28becf40-dc5f-4a1d-a1c9-5d6a41f797aa",
-   "metadata": {},
-   "source": [
-    "## Constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "739b109a-011b-4e6e-a3de-964edeffddbd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "NAN_THRESHOLD = 0.25\n",
-    "NUM_DAYS = 7\n",
-    "TRAIN_SIZE = 0.8\n",
-    "RANDOM_SEED = 85"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52d5aa23-6ae5-4ab6-824f-b68c47f471ae",
-   "metadata": {},
-   "source": [
-    "## Data Querying\n",
-    "\n",
-    "### Compute length of stay (labels)\n",
-    "\n",
-    "1. Get encounters, compute length of stay.\n",
-    "2. Filter out encounters less than 2 days and greater than 20 days."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e497df9f-0f3d-4e9c-845c-539627a37f67",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "querier = DatasetQuerier(\n",
-    "    dbms=\"postgresql\",\n",
-    "    port=5432,\n",
-    "    host=\"localhost\",\n",
-    "    database=\"synthea_demo\",\n",
-    "    user=\"postgres\",\n",
-    "    password=\"pwd\",\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def get_encounters():\n",
-    "    \"\"\"Get encounters data.\"\"\"\n",
-    "    patients = querier.native.patients()\n",
-    "    ops = qo.Sequential(\n",
-    "        qo.Rename({\"id\": \"patient_id\"}),\n",
-    "        qo.Keep([\"patient_id\", \"birthdate\", \"gender\", \"race\", \"ethnicity\"]),\n",
-    "    )\n",
-    "    patients = patients.ops(ops)\n",
-    "    encounters = querier.native.encounters()\n",
-    "    patient_encounters = encounters.join(\n",
-    "        patients,\n",
-    "        on=(\"patient\", \"patient_id\"),\n",
-    "        isouter=True,\n",
-    "    )\n",
-    "    ops = qo.Sequential(\n",
-    "        qo.Rename({\"id\": \"encounter_id\"}),\n",
-    "        qo.ExtractTimestampComponent(\"start\", \"year\", \"start_year\"),\n",
-    "        qo.ExtractTimestampComponent(\"birthdate\", \"year\", \"birthdate_year\"),\n",
-    "        qo.AddColumn(\n",
-    "            \"start_year\",\n",
-    "            \"birthdate_year\",\n",
-    "            new_col_labels=\"age\",\n",
-    "            negative=True,\n",
-    "        ),\n",
-    "        qo.AddColumn(\"stop\", \"start\", new_col_labels=\"los\", negative=True),\n",
-    "        qo.ConditionGreaterThan(\"los\", 1),\n",
-    "        qo.ConditionLessThan(\"los\", 21),\n",
-    "        qo.Keep(\n",
-    "            [\n",
-    "                \"encounter_id\",\n",
-    "                \"los\",\n",
-    "                \"age\",\n",
-    "                \"gender\",\n",
-    "            ],\n",
-    "        ),\n",
-    "    )\n",
-    "    return patient_encounters.ops(ops)\n",
-    "\n",
-    "\n",
-    "def get_observations(cohort):\n",
-    "    \"\"\"Get observations data.\"\"\"\n",
-    "    observations = querier.native.observations()\n",
-    "    ops = qo.Sequential(\n",
-    "        qo.ConditionIn(\n",
-    "            \"category\",\n",
-    "            [\n",
-    "                \"laboratory\",\n",
-    "                \"vital-signs\",\n",
-    "            ],\n",
-    "        ),\n",
-    "        qo.ConditionEquals(\"type\", \"numeric\"),\n",
-    "    )\n",
-    "    observations = observations.ops(ops)\n",
-    "    cohort = cohort.join(\n",
-    "        observations,\n",
-    "        on=(\"encounter_id\", \"encounter\"),\n",
-    "        isouter=True,\n",
-    "    )\n",
-    "    groupby_op = qo.GroupByAggregate(\n",
-    "        \"encounter_id\",\n",
-    "        {\"description\": (\"count\", \"n_obs\")},\n",
-    "    )\n",
-    "    observations = cohort.run()\n",
-    "    observations_count = cohort.ops(groupby_op).run()\n",
-    "    observations_stats = observations.pivot_table(\n",
-    "        index=\"encounter_id\",\n",
-    "        columns=\"description\",\n",
-    "        values=\"value\",\n",
-    "        aggfunc=\"max\",\n",
-    "    ).add_prefix(\"obs_\")\n",
-    "\n",
-    "    return [observations_count, observations_stats]\n",
-    "\n",
-    "\n",
-    "def get_medications(cohort):\n",
-    "    \"\"\"Get medications data.\"\"\"\n",
-    "    medications = querier.native.medications()\n",
-    "    cohort = cohort.join(\n",
-    "        medications,\n",
-    "        on=(\"encounter_id\", \"encounter\"),\n",
-    "    )\n",
-    "    groupby_op = qo.GroupByAggregate(\n",
-    "        \"encounter_id\",\n",
-    "        {\"description\": (\"count\", \"n_meds\")},\n",
-    "    )\n",
-    "\n",
-    "    return cohort.ops(groupby_op).run()\n",
-    "\n",
-    "\n",
-    "def get_procedures(cohort):\n",
-    "    \"\"\"Get procedures data.\"\"\"\n",
-    "    procedures = querier.native.procedures()\n",
-    "    cohort = cohort.join(\n",
-    "        procedures,\n",
-    "        on=(\"encounter_id\", \"encounter\"),\n",
-    "    )\n",
-    "    groupby_op = qo.GroupByAggregate(\n",
-    "        \"encounter_id\",\n",
-    "        {\"description\": (\"count\", \"n_procedures\")},\n",
-    "    )\n",
-    "\n",
-    "    return cohort.ops(groupby_op).run()\n",
-    "\n",
-    "\n",
-    "def run_query():\n",
-    "    \"\"\"Run query pipeline.\"\"\"\n",
-    "    cohort_query = get_encounters()\n",
-    "    to_merge = []\n",
-    "    observations = get_observations(cohort_query)\n",
-    "    to_merge.extend(observations)\n",
-    "    medications = get_medications(cohort_query)\n",
-    "    to_merge.append(medications)\n",
-    "    procedures = get_procedures(cohort_query)\n",
-    "    to_merge.append(procedures)\n",
-    "    cohort = cohort_query.run()\n",
-    "    for to_merge_df in to_merge:\n",
-    "        cohort = cohort.merge(\n",
-    "            to_merge_df,\n",
-    "            on=\"encounter_id\",\n",
-    "            how=\"left\",\n",
-    "        )\n",
-    "\n",
-    "    return cohort\n",
-    "\n",
-    "\n",
-    "cohort = run_query()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "389796a5-5abb-4c8f-bcb9-229d0e3e2108",
-   "metadata": {},
-   "source": [
-    "## Data Inspection and Preprocessing\n",
-    "\n",
-    "### Drop NaNs based on the `NAN_THRESHOLD`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c576ee51-e825-4970-86e8-3e5f221f145c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "null_counts = cohort.isnull().sum()[cohort.isnull().sum() > 0]\n",
-    "fig = go.Figure(data=[go.Bar(x=null_counts.index, y=null_counts.values)])\n",
-    "fig.update_layout(\n",
-    "    title=\"Number of Null Values per Column\",\n",
-    "    xaxis_title=\"Columns\",\n",
-    "    yaxis_title=\"Number of Null Values\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b77e5839-8c2f-4599-a511-2c3737e20772",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**\n",
-    "\n",
-    "We can use the log_plotly_figure method to add the figure to a section of the report. One can specify whether the figure should be interactive or not by setting the `interactive` parameter to `True` or `False` respectively. The default value is `True`. This\n",
-    "also affects the final size of the report. If the figure is interactive, the size of the report will be larger than if the figure is not interactive. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "190bcac3-2eda-4acc-bf1b-81cfd39a10b7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Number of Null Values per Column\",\n",
-    "    section_name=\"datasets\",\n",
-    "    interactive=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "32d67235-d839-4b70-bbd4-5126d8c6da49",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "thresh_nan = int(NAN_THRESHOLD * len(cohort))\n",
-    "cohort = cohort.dropna(axis=1, thresh=thresh_nan)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc5b45cb-2406-4330-b2fc-3b4823ff0c17",
-   "metadata": {},
-   "source": [
-    "### Length of stay distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f4cd2841-a992-4a89-936c-19bb90e6234d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "length_of_stay = cohort[\"los\"]\n",
-    "length_of_stay_counts = list(length_of_stay.value_counts().values)\n",
-    "length_of_stay_keys = list(length_of_stay.value_counts().keys())\n",
-    "cohort[\"outcome\"] = cohort[\"los\"] < NUM_DAYS\n",
-    "fig = go.Figure(data=[go.Bar(x=length_of_stay_keys, y=length_of_stay_counts)])\n",
-    "fig.update_layout(\n",
-    "    title=\"Length of stay\",\n",
-    "    xaxis_title=\"Days\",\n",
-    "    yaxis_title=\"Number of encounters\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc629738",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "57de20af",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Length of stay distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05156094-56e8-49c5-8e3c-478a1797db62",
-   "metadata": {},
-   "source": [
-    "### Outcome distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ae75c8a1-9316-4e47-ad45-7291a7b783bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cohort[\"outcome\"] = cohort[\"outcome\"].astype(\"int\")\n",
-    "fig = px.pie(cohort, names=\"outcome\")\n",
-    "fig.update_traces(textinfo=\"percent+label\")\n",
-    "fig.update_layout(title_text=\"Outcome Distribution\")\n",
-    "fig.update_traces(\n",
-    "    hovertemplate=\"Outcome: %{label}<br>Count: \\\n",
-    "    %{value}<br>Percent: %{percent}\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "742ba610-b754-458d-a77f-fc9469dcadab",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "794e085d-2dae-4dba-ade8-ab8cdc982af3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Outcome Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bab6a390-95f4-4876-a63d-210c95262960",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_counts = cohort[\"outcome\"].value_counts()\n",
-    "class_ratio = class_counts[0] / class_counts[1]\n",
-    "print(class_ratio)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e48376c2-a437-41f4-96fa-ea75f182f7b7",
-   "metadata": {},
-   "source": [
-    "### Gender distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d7ef4a36-3f9f-490a-befd-8e5fe41596ee",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.pie(cohort, names=\"gender\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Gender Distribution\",\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7b3673d-8df6-4aa8-9fff-f623f4d800ff",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8bd741fb-d0fe-4add-8d66-6b8baf271395",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Gender Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da0e832a-5763-42dc-a2e0-d91fef955ea5",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "###  Age distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ddb2b6cf-ee4d-4f83-8e56-6adcbe91f854",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fig = px.histogram(cohort, x=\"age\")\n",
-    "fig.update_layout(\n",
-    "    title=\"Age Distribution\",\n",
-    "    xaxis_title=\"Age\",\n",
-    "    yaxis_title=\"Count\",\n",
-    "    bargap=0.2,\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53519265-e826-459e-a6d6-de9c4cde41e7",
-   "metadata": {},
-   "source": [
-    "**Add the figure to the report**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f3c4c351-8eb5-41cd-8a50-006cfcfb13c0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "report.log_plotly_figure(\n",
-    "    fig=fig,\n",
-    "    caption=\"Age Distribution\",\n",
-    "    section_name=\"datasets\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "483c9bb5-57bf-4a2c-960f-35f7e76eff1d",
-   "metadata": {},
-   "source": [
-    "### Identifying feature types\n",
-    "\n",
-    "Cyclops `TabularFeatures` class helps to identify feature types, an essential step before preprocessing the data. Understanding feature types (numerical/categorical/binary) allows us to apply appropriate preprocessing steps for each type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2fb987af-f056-4886-84fa-4d4e1106e9b9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "features_list = [\n",
-    "    \"age\",\n",
-    "    \"gender\",\n",
-    "    \"n_obs\",\n",
-    "    \"n_meds\",\n",
-    "    \"n_procedures\",\n",
-    "    \"obs_Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma\",\n",
-    "    \"obs_Albumin [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma\",\n",
-    "    \"obs_Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma\",\n",
-    "    \"obs_Bilirubin.total [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Body Weight\",\n",
-    "    \"obs_Calcium [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Carbon dioxide  total [Moles/volume] in Serum or Plasma\",\n",
-    "    \"obs_Chloride [Moles/volume] in Serum or Plasma\",\n",
-    "    \"obs_Creatinine [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Diastolic Blood Pressure\",\n",
-    "    \"obs_Erythrocyte distribution width [Ratio] by Automated count\",\n",
-    "    \"obs_Erythrocytes [#/volume] in Blood by Automated count\",\n",
-    "    \"obs_Ferritin [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Glomerular filtration rate/1.73 sq M.predicted\",\n",
-    "    \"obs_Glucose [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Hematocrit [Volume Fraction] of Blood by Automated count\",\n",
-    "    \"obs_Hemoglobin [Mass/volume] in Blood\",\n",
-    "    \"obs_Leukocytes [#/volume] in Blood by Automated count\",\n",
-    "    \"obs_MCH [Entitic mass] by Automated count\",\n",
-    "    \"obs_MCHC [Mass/volume] by Automated count\",\n",
-    "    \"obs_MCV [Entitic volume] by Automated count\",\n",
-    "    \"obs_Oxygen saturation in Arterial blood\",\n",
-    "    \"obs_Platelets [#/volume] in Blood by Automated count\",\n",
-    "    \"obs_Potassium [Moles/volume] in Serum or Plasma\",\n",
-    "    \"obs_Protein [Mass/volume] in Serum or Plasma\",\n",
-    "    \"obs_Sodium [Moles/volume] in Serum or Plasma\",\n",
-    "    \"obs_Systolic Blood Pressure\",\n",
-    "    \"obs_Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method\",  # noqa: E501\n",
-    "    \"obs_Urea nitrogen [Mass/volume] in Serum or Plasma\",\n",
-    "]\n",
-    "features_list = sorted(features_list)\n",
-    "tab_features = TabularFeatures(\n",
-    "    data=cohort.reset_index(),\n",
-    "    features=features_list,\n",
-    "    by=\"encounter_id\",\n",
-    "    targets=\"outcome\",\n",
-    ")\n",
-    "print(tab_features.types)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2738074-00be-46fa-999f-77f85add9469",
-   "metadata": {},
-   "source": [
-    "### Creating data preprocessors\n",
-    "\n",
-    "We create a data preprocessor using sklearn's ColumnTransformer. This helps in applying different preprocessing steps to different columns in the dataframe. For instance, binary features might be processed differently from numeric features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2bb80cfb-2e48-4ecf-a325-0026dad4aef7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"mean\")), (\"scaler\", MinMaxScaler())],\n",
-    ")\n",
-    "\n",
-    "binary_transformer = Pipeline(\n",
-    "    steps=[(\"imputer\", SimpleImputer(strategy=\"most_frequent\"))],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "31ec3441-7dba-4b9a-9256-ad6b0293829d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "numeric_features = sorted((tab_features.features_by_type(\"numeric\")))\n",
-    "numeric_indices = [\n",
-    "    cohort[features_list].columns.get_loc(column) for column in numeric_features\n",
-    "]\n",
-    "print(numeric_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f2fb5345-17f9-44ac-b944-079324a098e4",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "binary_features = sorted(tab_features.features_by_type(\"binary\"))\n",
-    "binary_features.remove(\"outcome\")\n",
-    "binary_indices = [\n",
-    "    cohort[features_list].columns.get_loc(column) for column in binary_features\n",
-    "]\n",
-    "print(binary_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "868fa57c-f12a-4821-a85e-31c6c95d6e76",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "preprocessor = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        (\"num\", numeric_transformer, numeric_indices),\n",
-    "        (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"), binary_indices),\n",
-    "    ],\n",
-    "    remainder=\"passthrough\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a178f34-3883-43c2-8009-28063619df2c",
-   "metadata": {},
-   "source": [
-    "## Creating Hugging Face Dataset\n",
-    "\n",
-    "We convert our processed Pandas dataframe into a Hugging Face dataset, a powerful and easy-to-use data format which is also compatible with CyclOps models and evaluator modules. The dataset is then split to train and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "87b72a66-fc46-4e7a-a612-1f66de811c0d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "cohort = cohort.drop(columns=[\"encounter_id\", \"los\"])\n",
-    "dataset = Dataset.from_pandas(cohort)\n",
-    "dataset.cleanup_cache_files()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c6370e7a-c559-431f-9ebc-0b35bcdfb029",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = dataset.cast_column(\"outcome\", ClassLabel(num_classes=2))\n",
-    "dataset = dataset.train_test_split(\n",
-    "    train_size=TRAIN_SIZE,\n",
-    "    stratify_by_column=\"outcome\",\n",
-    "    seed=RANDOM_SEED,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71007875-2e2c-460f-a956-bcd419cb2017",
-   "metadata": {},
-   "source": [
-    "## Model Creation\n",
-    "\n",
-    "CyclOps model registry allows for straightforward creation and selection of models. This registry maintains a list of pre-configured models, which can be instantiated with a single line of code. Here we use a XGBoost classifier to fit a logisitic regression model. The model configurations can be passed to `create_model` based on the parameters for XGBClassifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "42e8d147-545c-47a7-9a91-b06b27a37d47",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_name = \"xgb_classifier\"\n",
-    "model = create_model(model_name, random_state=123)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff3ae174-59c0-41df-954c-f6e05f64c8dd",
-   "metadata": {},
-   "source": [
-    "## Task Creation\n",
-    "\n",
-    "We use Cyclops tasks to define our model's task (in this case, BinaryTabularClassificationTask), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "01d36851-292a-4849-98e0-9183c2dec87a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "los_task = BinaryTabularClassificationTask(\n",
-    "    {model_name: model},\n",
-    "    task_features=features_list,\n",
-    "    task_target=\"outcome\",\n",
-    ")\n",
-    "los_task.list_models()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cfb95f1e-ae78-469c-88d8-750d9c36a349",
-   "metadata": {},
-   "source": [
-    "## Training\n",
-    "\n",
-    "If `best_model_params` is passed to the `train` method, the best model will be selected after the hyperparameter search. The parameters in `best_model_params` indicate the values to create the parameters grid.\n",
-    "\n",
-    "Note that the data preprocessor needs to be passed to the tasks methods if the Hugging Face dataset is not already preprocessed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "572b0fe0-5891-4aca-9e63-8d556851caec",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "best_model_params = {\n",
-    "    \"n_estimators\": [100, 250, 500],\n",
-    "    \"learning_rate\": [0.1, 0.01],\n",
-    "    \"max_depth\": [2, 5],\n",
-    "    \"reg_lambda\": [0, 1, 10],\n",
-    "    \"colsample_bytree\": [0.7, 0.8, 1],\n",
-    "    \"gamma\": [0, 1, 2, 10],\n",
-    "    \"method\": \"random\",\n",
-    "}\n",
-    "los_task.train(\n",
-    "    dataset[\"train\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    best_model_params=best_model_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9b687a58-a6e5-46f7-bd9e-106376912086",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_params = los_task.list_models_params()[model_name]\n",
-    "print(model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e699187-2650-41ab-aba8-42bf9c635819",
-   "metadata": {},
-   "source": [
-    "**Log the model parameters to the report.**\n",
-    "\n",
-    "We can add model parameters to the model card using the `log_model_parameters` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4cfdfe29-c813-45cd-9ae7-2959daa0cd8f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_model_parameters(params=model_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d772526e-f595-4f4a-8026-7ab39523b3df",
-   "metadata": {},
-   "source": [
-    "## Prediction\n",
-    "\n",
-    "The prediction output can be either the whole Hugging Face dataset with the prediction columns added to it or the single column containing the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c3fee35c-902e-4c54-89b7-c42c1e935e1b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "y_pred = los_task.predict(\n",
-    "    dataset[\"test\"],\n",
-    "    model_name=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    proba=False,\n",
-    "    only_predictions=True,\n",
-    ")\n",
-    "print(len(y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2eb06bb-9c85-4597-a07d-f3f16c841c56",
-   "metadata": {},
-   "source": [
-    "## Evaluation\n",
-    "\n",
-    "Evaluation is done using various evaluation metrics that provide different perspectives on the model's predictive abilities i.e. standard performance metrics and fairness metrics.\n",
-    "\n",
-    "The standard performance metrics can be created using the `MetricDict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d3d5b101-17a8-4610-ae17-e04cf962d2d1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "metric_names = [\n",
-    "    \"binary_accuracy\",\n",
-    "    \"binary_precision\",\n",
-    "    \"binary_recall\",\n",
-    "    \"binary_f1_score\",\n",
-    "    \"binary_auroc\",\n",
-    "    \"binary_roc_curve\",\n",
-    "    \"binary_precision_recall_curve\",\n",
-    "    \"binary_confusion_matrix\",\n",
-    "]\n",
-    "metrics = [\n",
-    "    create_metric(metric_name, experimental=True) for metric_name in metric_names\n",
-    "]\n",
-    "metric_collection = MetricDict(metrics)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8cdf9238-dfc8-4eb2-a5c1-e0d273c75ee3",
-   "metadata": {},
-   "source": [
-    "In addition to overall metrics, it might be interesting to see how the model performs on certain subpopulations. We can define these subpopulations using `SliceSpec` objects. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "06d72639-db3b-4e91-be9d-3bf9dbb0f0e8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "spec_list = [\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"min_value\": 20,\n",
-    "            \"max_value\": 50,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"age\": {\n",
-    "            \"min_value\": 50,\n",
-    "            \"max_value\": 80,\n",
-    "            \"min_inclusive\": True,\n",
-    "            \"max_inclusive\": False,\n",
-    "        },\n",
-    "    },\n",
-    "    {\"gender\": {\"value\": \"M\"}},\n",
-    "    {\"gender\": {\"value\": \"F\"}},\n",
-    "]\n",
-    "slice_spec = SliceSpec(spec_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67bd7806-c480-4c47-8e33-6612c2ede93e",
-   "metadata": {},
-   "source": [
-    "A `MetricDict` can also be defined for the fairness metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "24877057-f737-43bf-a61d-3dd4e45e0381",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "specificity = create_metric(metric_name=\"binary_specificity\", experimental=True)\n",
-    "sensitivity = create_metric(metric_name=\"binary_sensitivity\", experimental=True)\n",
-    "fpr = (\n",
-    "    -specificity + 1\n",
-    ")  # rsub is not supported due to limitations in the array API standard\n",
-    "fnr = -sensitivity + 1\n",
-    "ber = (fpr + fnr) / 2\n",
-    "fairness_metric_collection = MetricDict(\n",
-    "    {\n",
-    "        \"Sensitivity\": sensitivity,\n",
-    "        \"Specificity\": specificity,\n",
-    "        \"BER\": ber,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b7f01c5-9598-4dcb-8697-ebc03a060d1c",
-   "metadata": {},
-   "source": [
-    "The FairnessConfig helps in setting up and evaluating the fairness of the model predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "945fdb5c-1b55-42f8-9644-e2bf6efd7011",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fairness_config = FairnessConfig(\n",
-    "    metrics=fairness_metric_collection,\n",
-    "    dataset=None,  # dataset is passed from the evaluator\n",
-    "    target_columns=None,  # target columns are passed from the evaluator\n",
-    "    groups=[\"gender\", \"age\"],\n",
-    "    group_bins={\"age\": [20, 40]},\n",
-    "    group_base_values={\"age\": 40, \"gender\": \"M\"},\n",
-    "    thresholds=[0.5],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e7d0840-f686-40c5-bb35-2ea37734553c",
-   "metadata": {},
-   "source": [
-    "The evaluate methods outputs the evaluation results and the Hugging Face dataset with the predictions added to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3797fe8c-886e-426f-8ea9-ad5afad3b65b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "results, dataset_with_preds = los_task.evaluate(\n",
-    "    dataset[\"test\"],\n",
-    "    metric_collection,\n",
-    "    model_names=model_name,\n",
-    "    transforms=preprocessor,\n",
-    "    prediction_column_prefix=\"preds\",\n",
-    "    slice_spec=slice_spec,\n",
-    "    batch_size=-1,\n",
-    "    fairness_config=fairness_config,\n",
-    "    override_fairness_metrics=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d2d1d75-f7d8-44d3-a782-2aba9a4fbac0",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "**Log the performance metrics to the report.**\n",
-    "\n",
-    "We can add a performance metric to the model card using the `log_performance_metric` method, which expects a dictionary where the keys are in the following format: `slice_name/metric_name`. For instance, `overall/accuracy`. \n",
-    "\n",
-    "We first need to process the evaluation results to get the metrics in the right format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d322a86f-1f7c-42f6-8a97-8a18ea8622e2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_name = f\"model_for_preds.{model_name}\"\n",
-    "results_flat = flatten_results_dict(\n",
-    "    results=results,\n",
-    "    remove_metrics=[\"BinaryROC\", \"BinaryPrecisionRecallCurve\"],\n",
-    "    model_name=model_name,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d33a171c-02ef-4bc9-a3bf-87320c7c83d6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "for name, metric in results_flat.items():\n",
-    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "    if name == \"BinaryConfusionMatrix\":\n",
-    "        continue\n",
-    "    descriptions = {\n",
-    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "    }\n",
-    "    report.log_quantitative_analysis(\n",
-    "        \"performance\",\n",
-    "        name=name,\n",
-    "        value=metric.tolist(),\n",
-    "        description=descriptions[name],\n",
-    "        metric_slice=split,\n",
-    "        pass_fail_thresholds=0.7,\n",
-    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ee464d3-7246-4c6f-ac2d-8cca2a985f22",
-   "metadata": {},
-   "source": [
-    "We can also use the `ClassificationPlotter` to plot the performance metrics and the add the figure to the model card using the `log_plotly_figure` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c1acc80b-9dce-4f10-9641-dba86cae0a50",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "plotter = ClassificationPlotter(task_type=\"binary\", class_names=[\"0\", \"1\"])\n",
-    "plotter.set_template(\"plotly_white\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba3a525b-63d5-4d35-b248-8fa67dc5a2c2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# extracting the ROC curves and AUROC results for all the slices\n",
-    "roc_curves = {\n",
-    "    slice_name: slice_results[\"BinaryROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "aurocs = {\n",
-    "    slice_name: slice_results[\"BinaryAUROC\"]\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}\n",
-    "roc_curves.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba032f62",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plot confusion matrix\n",
-    "confusion_matrix = results[model_name][\"overall\"][\"BinaryConfusionMatrix\"]\n",
-    "conf_plot = plotter.confusion_matrix(\n",
-    "    confusion_matrix,\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=conf_plot,\n",
-    "    caption=\"Confusion Matrix\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "conf_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9774f0ef-89a9-4c65-9021-635a90122676",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# plotting the ROC curves for all the slices\n",
-    "roc_plot = plotter.roc_curve_comparison(roc_curves, aurocs=aurocs)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=roc_plot,\n",
-    "    caption=\"ROC Curve for Female Patients\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "roc_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6d31a984-bd7f-419d-95dd-be4f397e853a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the overall classification metric values.\n",
-    "overall_performance = {\n",
-    "    metric_name: metric_value\n",
-    "    for metric_name, metric_value in results[model_name][\"overall\"].items()\n",
-    "    if metric_name\n",
-    "    not in [\"BinaryROC\", \"BinaryPrecisionRecallCurve\", \"BinaryConfusionMatrix\"]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a264f7a3-db58-4cbf-a67b-63a9ffe46b98",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the overall classification metric values.\n",
-    "overall_performance_plot = plotter.metrics_value(\n",
-    "    overall_performance,\n",
-    "    title=\"Overall Performance\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=overall_performance_plot,\n",
-    "    caption=\"Overall Performance\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "overall_performance_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a463b995-e2a3-4e2e-8339-7caa5d885482",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting the metric values for all the slices.\n",
-    "slice_metrics = {\n",
-    "    slice_name: {\n",
-    "        metric_name: metric_value\n",
-    "        for metric_name, metric_value in slice_results.items()\n",
-    "        if metric_name\n",
-    "        not in [\"BinaryROCCurve\", \"BinaryPrecisionRecallCurve\", \"BinaryConfusionMatrix\"]\n",
-    "    }\n",
-    "    for slice_name, slice_results in results[model_name].items()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a2f5b559-ec42-47f8-bf73-4326e595271b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the metric values for all the slices.\n",
-    "slice_metrics_plot = plotter.metrics_comparison_bar(slice_metrics)\n",
-    "report.log_plotly_figure(\n",
-    "    fig=slice_metrics_plot,\n",
-    "    caption=\"Slice Metric Comparison\",\n",
-    "    section_name=\"quantitative analysis\",\n",
-    ")\n",
-    "slice_metrics_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "22439e46-02a1-4ec6-83d5-9ab5ed9e57b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reformatting the fairness metrics\n",
-    "fairness_results = copy.deepcopy(results[\"fairness\"])\n",
-    "fairness_metrics = {}\n",
-    "# remove the group size from the fairness results and add it to the slice name\n",
-    "for slice_name, slice_results in fairness_results.items():\n",
-    "    group_size = slice_results.pop(\"Group Size\")\n",
-    "    fairness_metrics[f\"{slice_name} (Size={group_size})\"] = slice_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b82589f2-46e2-4936-871b-f26535a3e0c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plotting the fairness metrics\n",
-    "fairness_plot = plotter.metrics_comparison_scatter(\n",
-    "    fairness_metrics,\n",
-    "    title=\"Fairness Metrics\",\n",
-    ")\n",
-    "report.log_plotly_figure(\n",
-    "    fig=fairness_plot,\n",
-    "    caption=\"Fairness Metrics\",\n",
-    "    section_name=\"fairness analysis\",\n",
-    ")\n",
-    "fairness_plot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be00d959-442d-41f8-92fc-60abedef133a",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Report Generation\n",
-    "\n",
-    "Before generating the model card, let us document some of the details of the model and some considerations involved in developing and using the model.\n",
-    "\n",
-    "\n",
-    "Let's start with populating the model details section, which includes the following fields by default:\n",
-    "- description: A high-level description of the model and its usage for a general audience.\n",
-    "- version: The version of the model.\n",
-    "- owners: The individuals or organizations that own the model.\n",
-    "- license: The license under which the model is made available.\n",
-    "- citation: The citation for the model.\n",
-    "- references: Links to resources that are relevant to the model.\n",
-    "- path: The path to where the model is stored.\n",
-    "- regulatory_requirements: The regulatory requirements that are relevant to the model.\n",
-    "\n",
-    "We can add additional fields to the model details section by passing a dictionary to the `log_from_dict` method and specifying the section name as `model_details`. You can also use the `log_descriptor` method to add a new field object with a `description` attribute to any section of the model card."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5c68db3-e18b-43c1-b37e-ec6018cc03c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"name\": \"Prolonged Length of Stay Prediction Model\",\n",
-    "        \"description\": \"The model was trained on the Synthea synthetic dataset \\\n",
-    "            to predict prolonged stay in the hospital.\",\n",
-    "    },\n",
-    "    section_name=\"model_details\",\n",
-    ")\n",
-    "report.log_version(\n",
-    "    version_str=\"0.0.1\",\n",
-    "    date=str(date.today()),\n",
-    "    description=\"Initial Release\",\n",
-    ")\n",
-    "report.log_owner(\n",
-    "    name=\"CyclOps Team\",\n",
-    "    contact=\"vectorinstitute.github.io/cyclops/\",\n",
-    "    email=\"cyclops@vectorinstitute.ai\",\n",
-    ")\n",
-    "report.log_license(identifier=\"Apache-2.0\")\n",
-    "report.log_reference(\n",
-    "    link=\"https://xgboost.readthedocs.io/en/stable/python/python_api.html\",  # noqa: E501\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3cf7f12-db00-47b7-abb0-5259bce56500",
-   "metadata": {},
-   "source": [
-    "Next, let's populate the considerations section, which includes the following fields by default:\n",
-    "- users: The intended users of the model.\n",
-    "- use_cases: The use cases for the model. These could be primary, downstream or out-of-scope use cases.\n",
-    "- fairness_assessment: A description of the benefits and harms of the model for different groups as well as the steps taken to mitigate the harms.\n",
-    "- ethical_considerations: The risks associated with using the model and the steps taken to mitigate them. This can be populated using the  `log_risk` method.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4825be83-b601-4daa-94a8-c7819f53b22b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "report.log_from_dict(\n",
-    "    data={\n",
-    "        \"users\": [\n",
-    "            {\"description\": \"Hospitals\"},\n",
-    "            {\"description\": \"Clinicians\"},\n",
-    "        ],\n",
-    "    },\n",
-    "    section_name=\"considerations\",\n",
-    ")\n",
-    "report.log_user(description=\"ML Engineers\")\n",
-    "report.log_use_case(\n",
-    "    description=\"Predicting prolonged length of stay\",\n",
-    "    kind=\"primary\",\n",
-    ")\n",
-    "report.log_fairness_assessment(\n",
-    "    affected_group=\"sex, age\",\n",
-    "    benefit=\"Improved health outcomes for patients.\",\n",
-    "    harm=\"Biased predictions for patients in certain groups (e.g. older patients) \\\n",
-    "        may lead to worse health outcomes.\",\n",
-    "    mitigation_strategy=\"We will monitor the performance of the model on these groups \\\n",
-    "        and retrain the model if the performance drops below a certain threshold.\",\n",
-    ")\n",
-    "report.log_risk(\n",
-    "    risk=\"The model may be used to make decisions that affect the health of patients.\",\n",
-    "    mitigation_strategy=\"The model should be continuously monitored for performance \\\n",
-    "        and retrained if the performance drops below a certain threshold.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fea2ac13-7185-4327-9eef-e4cf08f3b3dd",
-   "metadata": {},
-   "source": [
-    "Once the model card is populated, you can generate the report using the `export` method. The report is generated in the form of an HTML file. A JSON file containing the model card data will also be generated along with the HTML file. By default, the files will be saved in a folder named `cyclops_reports` in the current working directory. You can change the path by passing a `output_dir` argument when instantiating the `ModelCardReport` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "257043a9-ae7b-4db6-a2f5-b03ff57320a7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "synthetic_timestamps = [\n",
-    "    \"2021-09-01\",\n",
-    "    \"2021-10-01\",\n",
-    "    \"2021-11-01\",\n",
-    "    \"2021-12-01\",\n",
-    "    \"2022-01-01\",\n",
-    "]\n",
-    "report._model_card.overview = None\n",
-    "report_path = report.export(\n",
-    "    output_filename=\"length_of_stay_report_periodic.html\",\n",
-    "    synthetic_timestamp=synthetic_timestamps[0],\n",
-    ")\n",
-    "shutil.copy(f\"{report_path}\", \".\")\n",
-    "for i in range(4):\n",
-    "    report._model_card.overview = None\n",
-    "    for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
-    "        metric.value = np.clip(\n",
-    "            metric.value + np.random.normal(0, 0.1),\n",
-    "            0,\n",
-    "            1,\n",
-    "        )\n",
-    "        metric.tests[0].passed = bool(metric.value >= 0.7)\n",
-    "    report_path = report.export(\n",
-    "        output_filename=\"length_of_stay_report_periodic.html\",\n",
-    "        synthetic_timestamp=synthetic_timestamps[i + 1],\n",
-    "    )\n",
-    "    shutil.copy(f\"{report_path}\", \".\")\n",
-    "shutil.rmtree(\"./cyclops_report\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d953f1b-845d-424e-b8b6-d782973d9e84",
-   "metadata": {},
-   "source": [
-    "You can view the generated HTML [report](./length_of_stay_report_periodic.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/api/tutorials_monitor.html b/api/tutorials_monitor.html
index cacbdc227..e57f2d5ab 100644
--- a/api/tutorials_monitor.html
+++ b/api/tutorials_monitor.html
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul>
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
@@ -333,17 +325,6 @@ <h1>monitor API<a class="headerlink" href="#monitor-api" title="Link to this hea
 It supports a number of dimensionality reduction techniques and statistical tests.
 Experiments can include sensitivity tests or rolling windows tests across time.</p>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials/nihcxr/monitor_api.html">NIHCXR Clinical Drift Experiments Tutorial</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/monitor_api.html#Import-Libraries-and-Load-NIHCXR-Dataset">Import Libraries and Load NIHCXR Dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/monitor_api.html#Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)">Example 1. Generate Source/Target Dataset for Experiments (1-2)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/monitor_api.html#Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques">Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/monitor_api.html#Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets">Example 3. Sensitivity test experiment with models trained on different datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/monitor_api.html#Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts">Example 4. Sensitivity test experiment with different clinical shifts</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/monitor_api.html#Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window">Example 5. Rolling window experiment with synthetic timestamps using biweekly window</a></li>
-</ul>
-</li>
-</ul>
 </div>
 </section>
 
diff --git a/api/tutorials_use_cases.html b/api/tutorials_use_cases.html
index f85f3b0d0..3a65970f5 100644
--- a/api/tutorials_use_cases.html
+++ b/api/tutorials_use_cases.html
@@ -7,7 +7,7 @@
 
     
       <meta name="description" content="CyclOps Python API documentation">
-    <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Heart Failure Prediction" href="tutorials/kaggle/heart_failure_prediction.html" /><link rel="prev" title="Tutorials" href="tutorials.html" />
+    <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="🧑🏿‍💻 Developing" href="developing.html" /><link rel="prev" title="Tutorials" href="tutorials.html" />
 
     <link rel="shortcut icon" href="_static/favicon.ico"/><meta name="generator" content="sphinx-7.2.6, furo 2024.01.29"/>
 
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User Guide</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l2 current has-children current-page"><a class="current reference internal" href="#">Example use cases</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 current has-children current-page"><a class="current reference internal" href="#">Example use cases</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
@@ -337,28 +329,6 @@ <h3>Kaggle Heart Failure Prediction<a class="headerlink" href="#kaggle-heart-fai
 is available on Kaggle. The dataset contains 11 features and 1 target
 variable.</p>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Import-Libraries">Import Libraries</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Constants">Constants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Data-Loading">Data Loading</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Sex-values">Sex values</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Age-distribution">Age distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Outcome-distribution">Outcome distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Identifying-feature-types">Identifying feature types</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Creating-data-preprocessors">Creating data preprocessors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Creating-Hugging-Face-Dataset">Creating Hugging Face Dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Model-Creation">Model Creation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Task-Creation">Task Creation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Training">Training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Prediction">Prediction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Evaluation">Evaluation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html#Report-Generation">Report Generation</a></li>
-</ul>
-</li>
-</ul>
 </div>
 </section>
 <section id="mimiciv-mortality-prediction">
@@ -366,33 +336,6 @@ <h3>MIMICIV Mortality Prediction<a class="headerlink" href="#mimiciv-mortality-p
 <p>This is a binary classification problem where the goal is to predict
 risk of in-hospital mortality. The <a class="reference external" href="https://mimic.mit.edu/">MIMICIV dataset</a> is an EHR dataset collected from a single hospital site, which includes ICU data.</p>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Import-Libraries">Import Libraries</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Constants">Constants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Data-Querying-&amp;-Processing">Data Querying &amp; Processing</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Compute-mortality-(labels)">Compute mortality (labels)</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Data-Inspection-and-Preprocessing">Data Inspection and Preprocessing</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Drop-NaNs-based-on-the-NAN_THRESHOLD">Drop NaNs based on the <code class="docutils literal notranslate"><span class="pre">NAN_THRESHOLD</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Outcome-distribution">Outcome distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Gender-distribution">Gender distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Age-distribution">Age distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Identifying-feature-types">Identifying feature types</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Creating-data-preprocessors">Creating data preprocessors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Creating-Hugging-Face-Dataset">Creating Hugging Face Dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Model-Creation">Model Creation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Task-Creation">Task Creation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Training">Training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Prediction">Prediction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Evaluation">Evaluation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html#Report-Generation">Report Generation</a></li>
-</ul>
-</li>
-</ul>
 </div>
 </section>
 <section id="synthea-prolonged-length-of-stay-prediction">
@@ -403,34 +346,6 @@ <h3>Synthea Prolonged Length of Stay Prediction<a class="headerlink" href="#synt
 is generated using Synthea which is a synthetic patient generator. The dataset
 contains observations, medications and procedures as features.</p>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Import-Libraries">Import Libraries</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Constants">Constants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Data-Querying">Data Querying</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Compute-length-of-stay-(labels)">Compute length of stay (labels)</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Data-Inspection-and-Preprocessing">Data Inspection and Preprocessing</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Drop-NaNs-based-on-the-NAN_THRESHOLD">Drop NaNs based on the <code class="docutils literal notranslate"><span class="pre">NAN_THRESHOLD</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Length-of-stay-distribution">Length of stay distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Outcome-distribution">Outcome distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Gender-distribution">Gender distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Age-distribution">Age distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Identifying-feature-types">Identifying feature types</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Creating-data-preprocessors">Creating data preprocessors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Creating-Hugging-Face-Dataset">Creating Hugging Face Dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Model-Creation">Model Creation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Task-Creation">Task Creation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Training">Training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Prediction">Prediction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Evaluation">Evaluation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/synthea/los_prediction.html#Report-Generation">Report Generation</a></li>
-</ul>
-</li>
-</ul>
 </div>
 </section>
 <section id="diabetes-130-us-hospitals-for-years-1999-2008-readmission-prediction">
@@ -440,29 +355,6 @@ <h3>Diabetes 130-US Hospitals for Years 1999-2008 Readmission Prediction<a class
 is available on UCI Machine Learning Repository. The dataset contains
 47 features and 1 target variable.</p>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Install-libraries">Install libraries</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Import-Libraries">Import Libraries</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Constants">Constants</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Data-Loading">Data Loading</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Sex-values">Sex values</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Age-distribution">Age distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Outcome-distribution">Outcome distribution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Identifying-feature-types">Identifying feature types</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Creating-data-preprocessors">Creating data preprocessors</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Creating-Hugging-Face-Dataset">Creating Hugging Face Dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Model-Creation">Model Creation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Task-Creation">Task Creation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Training">Training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Prediction">Prediction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Evaluation">Evaluation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html#Report-Generation">Report Generation</a></li>
-</ul>
-</li>
-</ul>
 </div>
 </section>
 </section>
@@ -475,26 +367,6 @@ <h3>NIH Chest X-ray classification<a class="headerlink" href="#nih-chest-x-ray-c
 <p>The tutorial also demonstrates the use of the <code class="docutils literal notranslate"><span class="pre">evaluate</span></code> API to evaluate the
 performance of a model on the task.</p>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html#Import-Libraries">Import Libraries</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html#Generate-Historical-Reports">Generate Historical Reports</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html#Initialize-Periodic-Report">Initialize Periodic Report</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html#Load-Dataset">Load Dataset</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html#Model-Creation">Model Creation</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html#Multilabel-AUROC-by-Pathology-and-Sex">Multilabel AUROC by Pathology and Sex</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html#Multilabel-AUROC-by-Pathology-and-Age">Multilabel AUROC by Pathology and Age</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html#Log-Performance-Metrics-as-Tests-w/-Thresholds">Log Performance Metrics as Tests w/ Thresholds</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html#Populate-Model-Card-Fields">Populate Model Card Fields</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
 </div>
 </section>
 </section>
@@ -505,12 +377,12 @@ <h3>NIH Chest X-ray classification<a class="headerlink" href="#nih-chest-x-ray-c
       <footer>
         
         <div class="related-pages">
-          <a class="next-page" href="tutorials/kaggle/heart_failure_prediction.html">
+          <a class="next-page" href="developing.html">
               <div class="page-info">
                 <div class="context">
                   <span>Next</span>
                 </div>
-                <div class="title">Heart Failure Prediction</div>
+                <div class="title">🧑🏿‍💻 Developing</div>
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
diff --git a/api/user_guide.html b/api/user_guide.html
index 0a1bd8586..31759c69d 100644
--- a/api/user_guide.html
+++ b/api/user_guide.html
@@ -61,7 +61,6 @@
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
     <script src="_static/copybutton.js?v=ccdb6887"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/require.min.js?v=53367eec"></script>
     <script src="_static/custom.js?v=275847af"></script>
     
@@ -192,24 +191,17 @@
   <ul class="current">
 <li class="toctree-l1 current has-children current-page"><a class="current reference internal" href="#">User Guide</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of User Guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="evaluation.html">Evaluation</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Evaluation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
-<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="model_report.html">Model Report</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Model Report</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="monitoring.html">Monitoring</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="tutorials.html">Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/kaggle/heart_failure_prediction.html">Heart Failure Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/mimiciv/mortality_prediction.html">Mortality Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/synthea/los_prediction.html">Prolonged Length of Stay Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/diabetes_130/readmission_prediction.html">Readmission Prediction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="tutorials/nihcxr/cxr_classification.html">Chest X-Ray Disease Classification</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="tutorials_use_cases.html">Example use cases</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Example use cases</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
 </ul>
 </li>
 </ul>
@@ -335,17 +327,7 @@ <h1>User Guide<a class="headerlink" href="#user-guide" title="Link to this headi
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="evaluation.html">Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="evaluation.html#key-capabilities">Key capabilities</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/metrics.html">Breast Cancer Classification and Evaluation</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples/metrics.html#Features">Features</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/metrics.html#Individual-Metrics">Individual Metrics</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/metrics.html#Using-MetricDict">Using <code class="docutils literal notranslate"><span class="pre">MetricDict</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/metrics.html#Data-Slicing">Data Slicing</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/metrics.html#Intersectional-slicing">Intersectional slicing</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/metrics.html#Preparing-Result">Preparing Result</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/metrics.html#Fairness-Evaluator">Fairness Evaluator</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="evaluation.html#key-capabilities">Key capabilities</a><ul class="simple">
 </ul>
 </li>
 </ul>
@@ -357,32 +339,7 @@ <h1>User Guide<a class="headerlink" href="#user-guide" title="Link to this headi
 <li class="toctree-l2"><a class="reference internal" href="model_report.html#fairness-analysis">Fairness Analysis</a></li>
 <li class="toctree-l2"><a class="reference internal" href="model_report.html#model-details">Model Details</a></li>
 <li class="toctree-l2"><a class="reference internal" href="model_report.html#model-parameters">Model Parameters</a></li>
-<li class="toctree-l2"><a class="reference internal" href="model_report.html#considerations">Considerations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/report.html">Report Generation for Heart Failure Prediction</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples/report.html#Create-Model-Card-Report">Create Model Card Report</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/report.html#Data-Loading">Data Loading</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/report.html#Adding-figures-to-report">Adding figures to report</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="examples/report.html#Age-distribution-figure">Age distribution figure</a></li>
-<li class="toctree-l5"><a class="reference internal" href="examples/report.html#Outcome-distribution">Outcome distribution</a></li>
-</ul>
-</li>
-<li class="toctree-l4"><a class="reference internal" href="examples/report.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/report.html#Create-HuggingFace-Dataset">Create HuggingFace Dataset</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/report.html#Training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/report.html#Evaluation">Evaluation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/report.html#Data-Slicing">Data Slicing</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="examples/report.html#Age-distribution-in-test-data">Age distribution in test data</a></li>
-</ul>
-</li>
-<li class="toctree-l4"><a class="reference internal" href="examples/report.html#Logging-metrics-and-results-to-report">Logging metrics and results to report</a></li>
-<li class="toctree-l4"><a class="reference internal" href="examples/report.html#Report-Generation">Report Generation</a><ul>
-<li class="toctree-l5"><a class="reference internal" href="examples/report.html#ModelCard">ModelCard</a></li>
-<li class="toctree-l5"><a class="reference internal" href="examples/report.html#Considerations">Considerations</a></li>
-<li class="toctree-l5"><a class="reference internal" href="examples/report.html#Exporting-report">Exporting report</a></li>
-</ul>
-</li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="model_report.html#considerations">Considerations</a><ul class="simple">
 </ul>
 </li>
 </ul>