diff --git a/api/examples/heart_failure_report_periodic.html b/api/examples/heart_failure_report_periodic.html
index 42d5c8cad..fc7293dcd 100644
--- a/api/examples/heart_failure_report_periodic.html
+++ b/api/examples/heart_failure_report_periodic.html
@@ -1064,23 +1064,23 @@ <h3>Sensitive Data</h3>
   
     
     <div class="subcard">
-      <h3>Reference</h3>
+      <h3>Citation</h3>
       <ul>
       
 <div>
   
+  <li>
     
-    <li>
       
-        
-          
-            <a href="https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction">https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction</a>
-          
-          <br>
-        
+        @misc{fedesoriano,
+  title={Heart Failure Prediction Dataset.},
+  author={Fedesoriano, F},
+  year={2021},
+  publisher={Kaggle}
+}
       
-    </li>
     
+  </li>
   
 </div>
 
@@ -1092,23 +1092,29 @@ <h3>Reference</h3>
   
     
     <div class="subcard">
-      <h3>Citation</h3>
+      <h3>License</h3>
       <ul>
       
 <div>
   
-  <li>
     
+    <li>
       
-        @misc{fedesoriano,
-  title={Heart Failure Prediction Dataset.},
-  author={Fedesoriano, F},
-  year={2021},
-  publisher={Kaggle}
-}
+        
+          
+            
+              <b>Identifier:</b> CC0-1.0
+            
+          
+          <br>
+        
+      
+        
+      
+        
       
+    </li>
     
-  </li>
   
 </div>
 
@@ -1120,7 +1126,7 @@ <h3>Citation</h3>
   
     
     <div class="subcard">
-      <h3>License</h3>
+      <h3>Reference</h3>
       <ul>
       
 <div>
@@ -1130,17 +1136,11 @@ <h3>License</h3>
       
         
           
-            
-              <b>Identifier:</b> CC0-1.0
-            
+            <a href="https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction">https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction</a>
           
           <br>
         
       
-        
-      
-        
-      
     </li>
     
   
@@ -1167,7 +1167,7 @@ <h3>Graphics</h3>
     
 <div class="img-item">
   
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@@ -1483,7 +1483,7 @@ <h3>Version</h3>
         
           
             
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diff --git a/api/examples/metrics.ipynb b/api/examples/metrics.ipynb
index ffbd92ea4..e3ea8a39b 100644
--- a/api/examples/metrics.ipynb
+++ b/api/examples/metrics.ipynb
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@@ -824,17 +824,17 @@
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diff --git a/api/examples/report.ipynb b/api/examples/report.ipynb
index d6e226a00..92d979e8b 100644
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@@ -100,10 +100,10 @@
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@@ -275,10 +275,10 @@
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@@ -420,10 +420,10 @@
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@@ -2312,10 +2312,10 @@
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@@ -4173,10 +4173,10 @@
    "execution_count": 10,
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@@ -4190,10 +4190,10 @@
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@@ -6019,10 +6019,10 @@
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@@ -6039,10 +6039,10 @@
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@@ -6333,10 +6333,10 @@
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@@ -6485,10 +6485,10 @@
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@@ -6520,10 +6520,10 @@
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@@ -6571,10 +6571,10 @@
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@@ -6600,10 +6600,10 @@
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@@ -6624,10 +6624,10 @@
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@@ -6661,10 +6661,10 @@
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@@ -6713,10 +6713,10 @@
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@@ -6754,10 +6754,10 @@
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@@ -6932,10 +6932,10 @@
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@@ -11136,10 +11136,10 @@
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-     "iopub.status.idle": "2024-06-22T15:18:21.991315Z",
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@@ -12039,9 +12039,9 @@
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"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 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"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, 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"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"]], 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\ No newline at end of file
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"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
diff --git a/api/tutorials/diabetes_130/readmission_prediction.html b/api/tutorials/diabetes_130/readmission_prediction.html
index a0bc76907..ad1ac1b17 100644
--- a/api/tutorials/diabetes_130/readmission_prediction.html
+++ b/api/tutorials/diabetes_130/readmission_prediction.html
@@ -365,6 +365,11 @@ <h2>Install libraries<a class="headerlink" href="#Install-libraries" title="Link
 Requirement already satisfied: matplotlib&lt;4.0.0,&gt;=3.8.3 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (3.8.3)
 Requirement already satisfied: numpy&lt;2.0.0,&gt;=1.24.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.24.4)
 Requirement already satisfied: pandas&lt;3.0,&gt;=2.1 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pandas[performance]&lt;3.0,&gt;=2.1-&gt;pycyclops) (2.1.4)
+Requirement already satisfied: pillow&lt;11.0.0,&gt;=10.0.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (10.3.0)
+Requirement already satisfied: plotly&lt;6.0.0,&gt;=5.7.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (5.18.0)
+Requirement already satisfied: psutil&lt;6.0.0,&gt;=5.9.4 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (5.9.7)
+Requirement already satisfied: pyarrow&lt;15.0.0,&gt;=14.0.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (14.0.2)
+Requirement already satisfied: pybtex&lt;0.25.0,&gt;=0.24.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (0.24.0)
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -372,11 +377,6 @@ <h2>Install libraries<a class="headerlink" href="#Install-libraries" title="Link
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Requirement already satisfied: pillow&lt;11.0.0,&gt;=10.0.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (10.3.0)
-Requirement already satisfied: plotly&lt;6.0.0,&gt;=5.7.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (5.18.0)
-Requirement already satisfied: psutil&lt;6.0.0,&gt;=5.9.4 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (5.9.7)
-Requirement already satisfied: pyarrow&lt;15.0.0,&gt;=14.0.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (14.0.2)
-Requirement already satisfied: pybtex&lt;0.25.0,&gt;=0.24.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (0.24.0)
 Requirement already satisfied: pydantic&lt;2.0.0,&gt;=1.10.11 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.10.13)
 Requirement already satisfied: scikit-learn&lt;2.0.0,&gt;=1.4.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.5.0)
 Requirement already satisfied: scipy&lt;2.0.0,&gt;=1.11.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.13.0rc1)
@@ -398,6 +398,9 @@ <h2>Install libraries<a class="headerlink" href="#Install-libraries" title="Link
 Requirement already satisfied: multiprocess in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets&lt;3.0.0,&gt;=2.15.0-&gt;pycyclops) (0.70.15)
 Requirement already satisfied: fsspec&lt;=2024.3.1,&gt;=2023.1.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from fsspec[http]&lt;=2024.3.1,&gt;=2023.1.0-&gt;datasets&lt;3.0.0,&gt;=2.15.0-&gt;pycyclops) (2023.10.0)
 Requirement already satisfied: aiohttp in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets&lt;3.0.0,&gt;=2.15.0-&gt;pycyclops) (3.9.5)
+Requirement already satisfied: huggingface-hub&gt;=0.21.2 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets&lt;3.0.0,&gt;=2.15.0-&gt;pycyclops) (0.22.2)
+Requirement already satisfied: packaging in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets&lt;3.0.0,&gt;=2.15.0-&gt;pycyclops) (23.2)
+Requirement already satisfied: pyyaml&gt;=5.1 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets&lt;3.0.0,&gt;=2.15.0-&gt;pycyclops) (6.0.1)
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -405,9 +408,6 @@ <h2>Install libraries<a class="headerlink" href="#Install-libraries" title="Link
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Requirement already satisfied: huggingface-hub&gt;=0.21.2 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets&lt;3.0.0,&gt;=2.15.0-&gt;pycyclops) (0.22.2)
-Requirement already satisfied: packaging in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets&lt;3.0.0,&gt;=2.15.0-&gt;pycyclops) (23.2)
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+{&#39;insulin&#39;: &#39;ordinal&#39;, &#39;diag_3&#39;: &#39;string&#39;, &#39;num_medications&#39;: &#39;numeric&#39;, &#39;diabetesMed&#39;: &#39;binary&#39;, &#39;glimepiride&#39;: &#39;ordinal&#39;, &#39;race&#39;: &#39;ordinal&#39;, &#39;metformin&#39;: &#39;ordinal&#39;, &#39;admission_source_id&#39;: &#39;ordinal&#39;, &#39;repaglinide&#39;: &#39;ordinal&#39;, &#39;diag_1&#39;: &#39;string&#39;, &#39;number_inpatient&#39;: &#39;ordinal&#39;, &#39;rosiglitazone&#39;: &#39;ordinal&#39;, &#39;tolazamide&#39;: &#39;binary&#39;, &#39;troglitazone&#39;: &#39;binary&#39;, &#39;tolbutamide&#39;: &#39;binary&#39;, &#39;diag_2&#39;: &#39;string&#39;, &#39;num_lab_procedures&#39;: &#39;numeric&#39;, &#39;number_outpatient&#39;: &#39;ordinal&#39;, &#39;number_diagnoses&#39;: &#39;ordinal&#39;, &#39;medical_specialty&#39;: &#39;string&#39;, &#39;num_procedures&#39;: &#39;ordinal&#39;, &#39;time_in_hospital&#39;: &#39;ordinal&#39;, &#39;max_glu_serum&#39;: &#39;ordinal&#39;, &#39;discharge_disposition_id&#39;: &#39;ordinal&#39;, &#39;gender&#39;: &#39;binary&#39;, &#39;admission_type_id&#39;: &#39;ordinal&#39;, &#39;A1Cresult&#39;: &#39;ordinal&#39;, &#39;outcome&#39;: &#39;binary&#39;, &#39;pioglitazone&#39;: &#39;ordinal&#39;, &#39;number_emergency&#39;: &#39;ordinal&#39;, &#39;glyburide&#39;: &#39;ordinal&#39;, &#39;change&#39;: &#39;binary&#39;, &#39;acarbose&#39;: &#39;binary&#39;, &#39;glipizide&#39;: &#39;ordinal&#39;, &#39;age&#39;: &#39;ordinal&#39;}
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 <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>
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diff --git a/api/tutorials/diabetes_130/readmission_prediction.ipynb b/api/tutorials/diabetes_130/readmission_prediction.ipynb
index 0bd1acd38..6c5c4326c 100644
--- a/api/tutorials/diabetes_130/readmission_prediction.ipynb
+++ b/api/tutorials/diabetes_130/readmission_prediction.ipynb
@@ -23,10 +23,10 @@
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-     "shell.execute_reply": "2024-06-22T15:19:26.512951Z"
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@@ -49,18 +49,18 @@
       "Requirement already satisfied: kaleido==0.2.1 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (0.2.1)\r\n",
       "Requirement already satisfied: matplotlib<4.0.0,>=3.8.3 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (3.8.3)\r\n",
       "Requirement already satisfied: numpy<2.0.0,>=1.24.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.24.4)\r\n",
-      "Requirement already satisfied: pandas<3.0,>=2.1 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pandas[performance]<3.0,>=2.1->pycyclops) (2.1.4)\r\n"
+      "Requirement already satisfied: pandas<3.0,>=2.1 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pandas[performance]<3.0,>=2.1->pycyclops) (2.1.4)\r\n",
+      "Requirement already satisfied: pillow<11.0.0,>=10.0.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (10.3.0)\r\n",
+      "Requirement already satisfied: plotly<6.0.0,>=5.7.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (5.18.0)\r\n",
+      "Requirement already satisfied: psutil<6.0.0,>=5.9.4 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (5.9.7)\r\n",
+      "Requirement already satisfied: pyarrow<15.0.0,>=14.0.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (14.0.2)\r\n",
+      "Requirement already satisfied: pybtex<0.25.0,>=0.24.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (0.24.0)\r\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: pillow<11.0.0,>=10.0.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (10.3.0)\r\n",
-      "Requirement already satisfied: plotly<6.0.0,>=5.7.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (5.18.0)\r\n",
-      "Requirement already satisfied: psutil<6.0.0,>=5.9.4 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (5.9.7)\r\n",
-      "Requirement already satisfied: pyarrow<15.0.0,>=14.0.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (14.0.2)\r\n",
-      "Requirement already satisfied: pybtex<0.25.0,>=0.24.0 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (0.24.0)\r\n",
       "Requirement already satisfied: pydantic<2.0.0,>=1.10.11 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.10.13)\r\n",
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@@ -80,16 +80,16 @@
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-      "Requirement already satisfied: aiohttp in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets<3.0.0,>=2.15.0->pycyclops) (3.9.5)\r\n"
+      "Requirement already satisfied: aiohttp in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets<3.0.0,>=2.15.0->pycyclops) (3.9.5)\r\n",
+      "Requirement already satisfied: huggingface-hub>=0.21.2 in /mnt/data/actions_runners/cyclops-actions-runner-2/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets<3.0.0,>=2.15.0->pycyclops) (0.22.2)\r\n",
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      "output_type": "stream",
      "text": [
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@@ -249,10 +249,10 @@
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@@ -318,10 +318,10 @@
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-     "iopub.execute_input": "2024-06-22T15:19:39.458325Z",
-     "iopub.status.busy": "2024-06-22T15:19:39.457130Z",
-     "iopub.status.idle": "2024-06-22T15:19:39.465279Z",
-     "shell.execute_reply": "2024-06-22T15:19:39.463731Z"
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@@ -341,10 +341,10 @@
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-     "shell.execute_reply": "2024-06-22T15:19:39.476717Z"
+     "iopub.execute_input": "2024-06-23T21:32:50.863797Z",
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@@ -368,10 +368,10 @@
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-     "shell.execute_reply": "2024-06-22T15:19:44.029837Z"
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@@ -389,10 +389,10 @@
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-     "shell.execute_reply": "2024-06-22T15:19:44.058866Z"
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@@ -451,10 +451,10 @@
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@@ -499,10 +499,10 @@
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@@ -522,10 +522,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T15:19:44.301294Z",
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-     "shell.execute_reply": "2024-06-22T15:19:44.365348Z"
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@@ -550,10 +550,10 @@
    "execution_count": 10,
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-     "shell.execute_reply": "2024-06-22T15:19:45.037205Z"
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        "                            \n",
-       "var gd = document.getElementById('55842dfe-39ce-4042-ae96-666a67849af1');\n",
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@@ -2496,10 +2496,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T15:19:45.126925Z",
-     "iopub.status.busy": "2024-06-22T15:19:45.125175Z",
-     "iopub.status.idle": "2024-06-22T15:19:45.140858Z",
-     "shell.execute_reply": "2024-06-22T15:19:45.139287Z"
+     "iopub.execute_input": "2024-06-23T21:32:56.376724Z",
+     "iopub.status.busy": "2024-06-23T21:32:56.375923Z",
+     "iopub.status.idle": "2024-06-23T21:32:56.391813Z",
+     "shell.execute_reply": "2024-06-23T21:32:56.390261Z"
     }
    },
    "outputs": [],
@@ -2523,10 +2523,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T15:19:45.148228Z",
-     "iopub.status.busy": "2024-06-22T15:19:45.146786Z",
-     "iopub.status.idle": "2024-06-22T15:19:45.317825Z",
-     "shell.execute_reply": "2024-06-22T15:19:45.316234Z"
+     "iopub.execute_input": "2024-06-23T21:32:56.398920Z",
+     "iopub.status.busy": "2024-06-23T21:32:56.397866Z",
+     "iopub.status.idle": "2024-06-23T21:32:56.567689Z",
+     "shell.execute_reply": "2024-06-23T21:32:56.565718Z"
     },
     "tags": []
    },
@@ -4411,9 +4411,9 @@
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@@ -4466,10 +4466,10 @@
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@@ -4493,10 +4493,10 @@
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@@ -4511,10 +4511,10 @@
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@@ -6376,9 +6376,9 @@
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@@ -6430,10 +6430,10 @@
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@@ -6450,10 +6450,10 @@
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@@ -6487,10 +6487,10 @@
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@@ -6515,10 +6515,10 @@
    "execution_count": 19,
    "metadata": {
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@@ -6527,7 +6527,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'acarbose': 'binary', 'diag_2': 'string', 'admission_source_id': 'ordinal', 'outcome': 'binary', 'discharge_disposition_id': 'ordinal', 'diag_3': 'string', 'troglitazone': 'binary', 'glyburide': 'ordinal', 'insulin': 'ordinal', 'diabetesMed': 'binary', 'race': 'ordinal', 'glimepiride': 'ordinal', 'number_diagnoses': 'ordinal', 'pioglitazone': 'ordinal', 'number_outpatient': 'ordinal', 'max_glu_serum': 'ordinal', 'age': 'ordinal', 'gender': 'binary', 'A1Cresult': 'ordinal', 'change': 'binary', 'num_procedures': 'ordinal', 'diag_1': 'string', 'tolazamide': 'binary', 'num_medications': 'numeric', 'metformin': 'ordinal', 'num_lab_procedures': 'numeric', 'number_inpatient': 'ordinal', 'time_in_hospital': 'ordinal', 'rosiglitazone': 'ordinal', 'number_emergency': 'ordinal', 'admission_type_id': 'ordinal', 'medical_specialty': 'string', 'glipizide': 'ordinal', 'repaglinide': 'ordinal', 'tolbutamide': 'binary'}\n"
+      "{'insulin': 'ordinal', 'diag_3': 'string', 'num_medications': 'numeric', 'diabetesMed': 'binary', 'glimepiride': 'ordinal', 'race': 'ordinal', 'metformin': 'ordinal', 'admission_source_id': 'ordinal', 'repaglinide': 'ordinal', 'diag_1': 'string', 'number_inpatient': 'ordinal', 'rosiglitazone': 'ordinal', 'tolazamide': 'binary', 'troglitazone': 'binary', 'tolbutamide': 'binary', 'diag_2': 'string', 'num_lab_procedures': 'numeric', 'number_outpatient': 'ordinal', 'number_diagnoses': 'ordinal', 'medical_specialty': 'string', 'num_procedures': 'ordinal', 'time_in_hospital': 'ordinal', 'max_glu_serum': 'ordinal', 'discharge_disposition_id': 'ordinal', 'gender': 'binary', 'admission_type_id': 'ordinal', 'A1Cresult': 'ordinal', 'outcome': 'binary', 'pioglitazone': 'ordinal', 'number_emergency': 'ordinal', 'glyburide': 'ordinal', 'change': 'binary', 'acarbose': 'binary', 'glipizide': 'ordinal', 'age': 'ordinal'}\n"
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@@ -6555,10 +6555,10 @@
    "execution_count": 20,
    "metadata": {
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@@ -6578,10 +6578,10 @@
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-     "shell.execute_reply": "2024-06-22T15:19:45.810676Z"
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@@ -6607,10 +6607,10 @@
    "execution_count": 22,
    "metadata": {
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-     "shell.execute_reply": "2024-06-22T15:19:45.920826Z"
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@@ -6644,10 +6644,10 @@
    "execution_count": 23,
    "metadata": {
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-     "iopub.execute_input": "2024-06-22T15:19:45.931026Z",
-     "iopub.status.busy": "2024-06-22T15:19:45.929484Z",
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-     "shell.execute_reply": "2024-06-22T15:19:45.937889Z"
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@@ -6687,10 +6687,10 @@
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    "metadata": {
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-     "iopub.execute_input": "2024-06-22T15:19:45.947445Z",
-     "iopub.status.busy": "2024-06-22T15:19:45.946529Z",
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-     "shell.execute_reply": "2024-06-22T15:19:46.178737Z"
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@@ -6734,10 +6734,10 @@
    "execution_count": 25,
    "metadata": {
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-     "iopub.execute_input": "2024-06-22T15:19:46.188918Z",
-     "iopub.status.busy": "2024-06-22T15:19:46.187774Z",
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-     "shell.execute_reply": "2024-06-22T15:19:46.422278Z"
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@@ -6764,10 +6764,10 @@
    "execution_count": 26,
    "metadata": {
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-     "iopub.execute_input": "2024-06-22T15:19:46.431876Z",
-     "iopub.status.busy": "2024-06-22T15:19:46.430266Z",
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@@ -6775,7 +6775,7 @@
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@@ -6810,10 +6810,10 @@
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-     "iopub.status.busy": "2024-06-22T15:19:46.573530Z",
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-     "shell.execute_reply": "2024-06-22T15:19:49.561806Z"
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@@ -6837,10 +6837,10 @@
    "execution_count": 28,
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-     "iopub.execute_input": "2024-06-22T15:19:49.571751Z",
-     "iopub.status.busy": "2024-06-22T15:19:49.570315Z",
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-     "shell.execute_reply": "2024-06-22T15:19:49.577060Z"
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@@ -6858,10 +6858,10 @@
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-     "iopub.status.busy": "2024-06-22T15:19:49.584457Z",
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-     "shell.execute_reply": "2024-06-22T15:19:49.593000Z"
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@@ -6897,10 +6897,10 @@
    "execution_count": 30,
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-     "iopub.execute_input": "2024-06-22T15:19:49.601886Z",
-     "iopub.status.busy": "2024-06-22T15:19:49.600636Z",
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-     "shell.execute_reply": "2024-06-22T15:26:08.728232Z"
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@@ -6909,49 +6909,49 @@
      "name": "stderr",
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      "text": [
-      "2024-06-22 11:26:08,664 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best scale_pos_weight: 9\n"
+      "2024-06-23 17:50:07,717 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best scale_pos_weight: 9\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:26:08,672 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 1\n"
+      "2024-06-23 17:50:07,722 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:26:08,674 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 500\n"
+      "2024-06-23 17:50:07,724 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 500\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:26:08,675 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
+      "2024-06-23 17:50:07,725 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:26:08,676 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
+      "2024-06-23 17:50:07,726 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:26:08,678 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 0\n"
+      "2024-06-23 17:50:07,728 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 0\n"
      ]
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     {
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      "output_type": "stream",
      "text": [
-      "2024-06-22 11:26:08,679 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.7\n"
+      "2024-06-23 17:50:07,729 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.7\n"
      ]
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     {
@@ -7426,10 +7426,10 @@
    "execution_count": 31,
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-     "shell.execute_reply": "2024-06-22T15:26:08.759272Z"
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     }
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@@ -7437,7 +7437,7 @@
      "name": "stdout",
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      "text": [
-      "{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.7, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 0, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 1, 'sampling_method': None, 'scale_pos_weight': 9, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}\n"
+      "{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.7, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 0, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': 9, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}\n"
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@@ -7460,10 +7460,10 @@
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-     "shell.execute_reply": "2024-06-22T15:26:08.778723Z"
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     }
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@@ -7485,10 +7485,10 @@
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-     "iopub.status.busy": "2024-06-22T15:26:08.786189Z",
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-     "shell.execute_reply": "2024-06-22T15:26:09.177931Z"
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@@ -7496,7 +7496,7 @@
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@@ -7541,10 +7541,10 @@
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-     "iopub.status.busy": "2024-06-22T15:26:09.186761Z",
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-     "shell.execute_reply": "2024-06-22T15:26:09.265170Z"
+     "iopub.execute_input": "2024-06-23T21:50:08.181519Z",
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@@ -7578,10 +7578,10 @@
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-     "iopub.execute_input": "2024-06-22T15:26:09.274495Z",
-     "iopub.status.busy": "2024-06-22T15:26:09.273342Z",
-     "iopub.status.idle": "2024-06-22T15:26:09.283942Z",
-     "shell.execute_reply": "2024-06-22T15:26:09.282358Z"
+     "iopub.execute_input": "2024-06-23T21:50:08.236209Z",
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@@ -7614,10 +7614,10 @@
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-     "iopub.execute_input": "2024-06-22T15:26:09.291006Z",
-     "iopub.status.busy": "2024-06-22T15:26:09.289926Z",
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-     "shell.execute_reply": "2024-06-22T15:26:09.315205Z"
+     "iopub.execute_input": "2024-06-23T21:50:08.251650Z",
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@@ -7652,10 +7652,10 @@
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-     "iopub.status.busy": "2024-06-22T15:26:09.322871Z",
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-     "shell.execute_reply": "2024-06-22T15:26:09.330079Z"
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@@ -7683,10 +7683,10 @@
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-     "iopub.status.busy": "2024-06-22T15:26:09.337340Z",
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-     "shell.execute_reply": "2024-06-22T15:26:10.945090Z"
+     "iopub.execute_input": "2024-06-23T21:50:08.292957Z",
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@@ -7694,7 +7694,7 @@
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@@ -7708,7 +7708,7 @@
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@@ -7722,7 +7722,7 @@
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@@ -7736,7 +7736,7 @@
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@@ -7750,7 +7750,7 @@
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@@ -7764,7 +7764,7 @@
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@@ -7778,7 +7778,7 @@
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@@ -7792,7 +7792,7 @@
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@@ -11251,10 +11251,10 @@
    "execution_count": 47,
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-     "shell.execute_reply": "2024-06-22T15:26:12.253691Z"
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+     "shell.execute_reply": "2024-06-23T21:50:11.067900Z"
     }
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@@ -11276,12 +11276,12 @@
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@@ -12118,9 +12118,9 @@
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@@ -12168,10 +12168,10 @@
    "execution_count": 48,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T15:26:12.263619Z",
-     "iopub.status.busy": "2024-06-22T15:26:12.262461Z",
-     "iopub.status.idle": "2024-06-22T15:26:12.271974Z",
-     "shell.execute_reply": "2024-06-22T15:26:12.270411Z"
+     "iopub.execute_input": "2024-06-23T21:50:11.077763Z",
+     "iopub.status.busy": "2024-06-23T21:50:11.076586Z",
+     "iopub.status.idle": "2024-06-23T21:50:11.086152Z",
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@@ -12192,10 +12192,10 @@
    "execution_count": 49,
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@@ -17275,10 +17275,10 @@
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@@ -17297,10 +17297,10 @@
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 <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>
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diff --git a/api/tutorials/kaggle/heart_failure_prediction.ipynb b/api/tutorials/kaggle/heart_failure_prediction.ipynb
index 6ce33bf55..b99264eea 100644
--- a/api/tutorials/kaggle/heart_failure_prediction.ipynb
+++ b/api/tutorials/kaggle/heart_failure_prediction.ipynb
@@ -21,10 +21,10 @@
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@@ -165,10 +165,10 @@
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@@ -177,7 +177,7 @@
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-      "2024-06-22 11:26:41,328 \u001b[1;37mINFO\u001b[0m cyclops.utils.file - Loading DataFrame from ./data/heart.csv\n"
+      "2024-06-23 17:50:38,983 \u001b[1;37mINFO\u001b[0m cyclops.utils.file - Loading DataFrame from ./data/heart.csv\n"
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@@ -2124,10 +2124,10 @@
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@@ -3985,10 +3985,10 @@
    "execution_count": 9,
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-     "shell.execute_reply": "2024-06-22T15:26:42.449916Z"
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@@ -4012,10 +4012,10 @@
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@@ -4030,10 +4030,10 @@
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        "                            \n",
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@@ -5867,10 +5867,10 @@
    "execution_count": 12,
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@@ -5887,10 +5887,10 @@
    "execution_count": 13,
    "metadata": {
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@@ -5917,10 +5917,10 @@
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-     "shell.execute_reply": "2024-06-22T15:26:42.648437Z"
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@@ -5952,10 +5952,10 @@
    "execution_count": 15,
    "metadata": {
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-     "shell.execute_reply": "2024-06-22T15:26:42.662941Z"
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@@ -5992,10 +5992,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T15:26:42.671655Z",
-     "iopub.status.busy": "2024-06-22T15:26:42.670367Z",
-     "iopub.status.idle": "2024-06-22T15:26:42.738379Z",
-     "shell.execute_reply": "2024-06-22T15:26:42.736714Z"
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@@ -6004,7 +6004,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'outcome': 'binary', 'Oldpeak': 'numeric', 'ChestPainType': 'ordinal', 'ST_Slope': 'ordinal', 'FastingBS': 'binary', 'RestingBP': 'numeric', 'RestingECG': 'ordinal', 'ExerciseAngina': 'binary', 'Age': 'numeric', 'MaxHR': 'numeric', 'Cholesterol': 'numeric', 'Sex': 'binary'}\n"
+      "{'RestingECG': 'ordinal', 'FastingBS': 'binary', 'MaxHR': 'numeric', 'ExerciseAngina': 'binary', 'ST_Slope': 'ordinal', 'ChestPainType': 'ordinal', 'Cholesterol': 'numeric', 'Sex': 'binary', 'Oldpeak': 'numeric', 'Age': 'numeric', 'outcome': 'binary', 'RestingBP': 'numeric'}\n"
      ]
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    ],
@@ -6032,10 +6032,10 @@
    "execution_count": 17,
    "metadata": {
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-     "iopub.status.busy": "2024-06-22T15:26:42.744457Z",
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-     "shell.execute_reply": "2024-06-22T15:26:42.751946Z"
+     "iopub.execute_input": "2024-06-23T21:50:40.386892Z",
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@@ -6055,10 +6055,10 @@
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    "metadata": {
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-     "shell.execute_reply": "2024-06-22T15:26:42.781389Z"
+     "iopub.execute_input": "2024-06-23T21:50:40.403132Z",
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@@ -6084,10 +6084,10 @@
    "execution_count": 19,
    "metadata": {
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-     "iopub.status.idle": "2024-06-22T15:26:42.816973Z",
-     "shell.execute_reply": "2024-06-22T15:26:42.815354Z"
+     "iopub.execute_input": "2024-06-23T21:50:40.433888Z",
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@@ -6118,10 +6118,10 @@
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@@ -6161,10 +6161,10 @@
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@@ -6213,10 +6213,10 @@
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@@ -6243,10 +6243,10 @@
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@@ -6254,7 +6254,7 @@
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@@ -6289,10 +6289,10 @@
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@@ -6316,10 +6316,10 @@
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@@ -6337,10 +6337,10 @@
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@@ -6376,10 +6376,10 @@
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@@ -6388,21 +6388,21 @@
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-      "2024-06-22 11:27:03,972 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best alpha: 0.001\n"
+      "2024-06-23 17:51:01,714 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best alpha: 0.001\n"
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+      "2024-06-23 17:51:01,718 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best eta0: 0.01\n"
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-      "2024-06-22 11:27:03,975 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: adaptive\n"
+      "2024-06-23 17:51:01,719 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: adaptive\n"
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@@ -6851,10 +6851,10 @@
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@@ -6885,10 +6885,10 @@
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@@ -6910,10 +6910,10 @@
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@@ -6921,7 +6921,7 @@
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@@ -6966,10 +6966,10 @@
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@@ -7003,10 +7003,10 @@
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@@ -7046,10 +7046,10 @@
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@@ -7084,10 +7084,10 @@
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@@ -7116,10 +7116,10 @@
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@@ -7127,7 +7127,7 @@
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@@ -7141,7 +7141,7 @@
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@@ -7155,7 +7155,7 @@
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@@ -7169,7 +7169,7 @@
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@@ -7183,7 +7183,7 @@
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@@ -7197,7 +7197,7 @@
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@@ -7211,7 +7211,7 @@
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@@ -7225,7 +7225,7 @@
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@@ -7239,7 +7239,7 @@
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@@ -7253,7 +7253,7 @@
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@@ -7267,7 +7267,7 @@
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@@ -7298,17 +7298,17 @@
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@@ -7322,7 +7322,7 @@
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@@ -7336,7 +7336,7 @@
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@@ -7350,7 +7350,7 @@
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@@ -7364,7 +7364,7 @@
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@@ -7378,7 +7378,7 @@
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@@ -7427,10 +7427,10 @@
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@@ -7499,10 +7499,10 @@
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@@ -7516,10 +7516,10 @@
    "execution_count": 39,
    "metadata": {
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    "outputs": [
@@ -7552,10 +7552,10 @@
    "execution_count": 40,
    "metadata": {
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@@ -9513,10 +9513,10 @@
    "execution_count": 41,
    "metadata": {
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-     "iopub.status.idle": "2024-06-22T15:27:06.034343Z",
-     "shell.execute_reply": "2024-06-22T15:27:06.032582Z"
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    "outputs": [
@@ -9549,10 +9549,10 @@
    "execution_count": 42,
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-     "shell.execute_reply": "2024-06-22T15:27:06.104284Z"
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    "outputs": [
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@@ -11518,10 +11518,10 @@
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@@ -12435,10 +12435,10 @@
    "execution_count": 45,
    "metadata": {
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-     "iopub.status.idle": "2024-06-22T15:27:06.205147Z",
-     "shell.execute_reply": "2024-06-22T15:27:06.203466Z"
+     "iopub.execute_input": "2024-06-23T21:51:03.968661Z",
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@@ -12459,10 +12459,10 @@
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@@ -17428,10 +17428,10 @@
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@@ -1338,7 +1338,7 @@ <h3>Sensitive Data</h3>
   
     
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-            
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-        @misc{fedesoriano,
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-          
-          <br>
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+        @misc{fedesoriano,
+  title={Heart Failure Prediction Dataset.},
+  author={Fedesoriano, F},
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@@ -1886,7 +1886,7 @@ <h3>Version</h3>
         
           
             
-              <b>Date:</b> 2024-06-22
+              <b>Date:</b> 2024-06-23
             
           
           <br>
@@ -2105,7 +2105,7 @@ <h2 class="collapsible">Model Parameters</h2>
   
     
     <div class="subcard">
-      <h3>Alpha</h3>
+      <h3>Tol</h3>
       0.001
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@@ -2114,8 +2114,8 @@ <h3>Alpha</h3>
   
     
     <div class="subcard">
-      <h3>L1_ratio</h3>
-      0.15
+      <h3>Fit_intercept</h3>
+      True
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@@ -2123,8 +2123,8 @@ <h3>L1_ratio</h3>
   
     
     <div class="subcard">
-      <h3>Shuffle</h3>
-      True
+      <h3>Alpha</h3>
+      0.001
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@@ -2132,7 +2132,7 @@ <h3>Shuffle</h3>
   
     
     <div class="subcard">
-      <h3>Epsilon</h3>
+      <h3>Validation_fraction</h3>
       0.1
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@@ -2141,8 +2141,8 @@ <h3>Epsilon</h3>
   
     
     <div class="subcard">
-      <h3>Fit_intercept</h3>
-      True
+      <h3>N_iter_no_change</h3>
+      5
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@@ -2150,8 +2150,8 @@ <h3>Fit_intercept</h3>
   
     
     <div class="subcard">
-      <h3>Verbose</h3>
-      0
+      <h3>Power_t</h3>
+      0.5
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@@ -2159,8 +2159,8 @@ <h3>Verbose</h3>
   
     
     <div class="subcard">
-      <h3>Class_weight</h3>
-      balanced
+      <h3>Shuffle</h3>
+      True
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@@ -2168,8 +2168,8 @@ <h3>Class_weight</h3>
   
     
     <div class="subcard">
-      <h3>Eta0</h3>
-      0.01
+      <h3>Average</h3>
+      False
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     <div class="subcard">
-      <h3>Max_iter</h3>
-      1000
+      <h3>Penalty</h3>
+      l2
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@@ -2186,8 +2186,8 @@ <h3>Max_iter</h3>
   
     
     <div class="subcard">
-      <h3>Random_state</h3>
-      123
+      <h3>L1_ratio</h3>
+      0.15
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@@ -2195,8 +2195,8 @@ <h3>Random_state</h3>
   
     
     <div class="subcard">
-      <h3>Average</h3>
-      False
+      <h3>Early_stopping</h3>
+      True
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@@ -2204,8 +2204,8 @@ <h3>Average</h3>
   
     
     <div class="subcard">
-      <h3>Learning_rate</h3>
-      adaptive
+      <h3>Max_iter</h3>
+      1000
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@@ -2213,8 +2213,8 @@ <h3>Learning_rate</h3>
   
     
     <div class="subcard">
-      <h3>Warm_start</h3>
-      False
+      <h3>Learning_rate</h3>
+      adaptive
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@@ -2222,8 +2222,8 @@ <h3>Warm_start</h3>
   
     
     <div class="subcard">
-      <h3>Penalty</h3>
-      l2
+      <h3>Verbose</h3>
+      0
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@@ -2231,8 +2231,8 @@ <h3>Penalty</h3>
   
     
     <div class="subcard">
-      <h3>Loss</h3>
-      log_loss
+      <h3>Epsilon</h3>
+      0.1
     </div>
     
   
@@ -2240,40 +2240,40 @@ <h3>Loss</h3>
   
     
     <div class="subcard">
-      <h3>Power_t</h3>
-      0.5
+      <h3>Loss</h3>
+      log_loss
     </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Early_stopping</h3>
-      True
-    </div>
     
   
 
   
     
     <div class="subcard">
-      <h3>Validation_fraction</h3>
-      0.1
+      <h3>Eta0</h3>
+      0.01
     </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Class_weight</h3>
+      balanced
+    </div>
     
   
 
   
     
     <div class="subcard">
-      <h3>N_iter_no_change</h3>
-      5
+      <h3>Warm_start</h3>
+      False
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@@ -2281,8 +2281,8 @@ <h3>N_iter_no_change</h3>
   
     
     <div class="subcard">
-      <h3>Tol</h3>
-      0.001
+      <h3>Random_state</h3>
+      123
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diff --git a/api/tutorials/mimiciv/mortality_prediction.html b/api/tutorials/mimiciv/mortality_prediction.html
index ba7ba5b02..f83a2a2b4 100644
--- a/api/tutorials/mimiciv/mortality_prediction.html
+++ b/api/tutorials/mimiciv/mortality_prediction.html
@@ -603,7 +603,7 @@ <h3>Compute mortality (labels)<a class="headerlink" href="#Compute-mortality-(la
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 11:27:44,063 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Database setup, ready to run queries!
+2024-06-23 17:51:40,599 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Database setup, ready to run queries!
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -611,7 +611,7 @@ <h3>Compute mortality (labels)<a class="headerlink" href="#Compute-mortality-(la
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 11:28:05,493 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-23 17:51:59,829 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
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 </div>
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@@ -619,7 +619,7 @@ <h3>Compute mortality (labels)<a class="headerlink" href="#Compute-mortality-(la
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 11:28:05,496 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 19.191247 s
+2024-06-23 17:51:59,831 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 16.893647 s
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -646,7 +646,7 @@ <h3>Compute mortality (labels)<a class="headerlink" href="#Compute-mortality-(la
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 11:30:49,671 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-23 17:54:34,379 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -654,7 +654,7 @@ <h3>Compute mortality (labels)<a class="headerlink" href="#Compute-mortality-(la
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 11:30:49,673 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 161.002521 s
+2024-06-23 17:54:34,382 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 151.679064 s
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -726,9 +726,9 @@ <h3>Drop NaNs based on the <code class="docutils literal notranslate"><span clas
 <div class="prompt empty docutils container">
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@@ -1003,7 +1003,7 @@ <h3>Identifying feature types<a class="headerlink" href="#Identifying-feature-ty
 </div>
 <div class="output_area docutils container">
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-{&#39;lab_calcium, total&#39;: &#39;numeric&#39;, &#39;lab_alkaline phosphatase&#39;: &#39;numeric&#39;, &#39;lab_hematocrit&#39;: &#39;numeric&#39;, &#39;lab_chloride&#39;: &#39;numeric&#39;, &#39;lab_hemoglobin&#39;: &#39;numeric&#39;, &#39;lab_inr(pt)&#39;: &#39;numeric&#39;, &#39;mortality_outcome&#39;: &#39;binary&#39;, &#39;lab_pt&#39;: &#39;numeric&#39;, &#39;lab_l&#39;: &#39;numeric&#39;, &#39;lab_phosphate&#39;: &#39;numeric&#39;, &#39;lab_urea nitrogen&#39;: &#39;numeric&#39;, &#39;lab_alanine aminotransferase (alt)&#39;: &#39;numeric&#39;, &#39;lab_h&#39;: &#39;numeric&#39;, &#39;lab_bicarbonate&#39;: &#39;numeric&#39;, &#39;lab_mchc&#39;: &#39;numeric&#39;, &#39;lab_white blood cells&#39;: &#39;numeric&#39;, &#39;lab_creatinine&#39;: &#39;numeric&#39;, &#39;lab_rdw&#39;: &#39;numeric&#39;, &#39;lab_glucose&#39;: &#39;numeric&#39;, &#39;lab_bilirubin, total&#39;: &#39;numeric&#39;, &#39;lab_anion gap&#39;: &#39;numeric&#39;, &#39;lab_asparate aminotransferase (ast)&#39;: &#39;numeric&#39;, &#39;lab_ph&#39;: &#39;numeric&#39;, &#39;lab_magnesium&#39;: &#39;numeric&#39;, &#39;lab_sodium&#39;: &#39;numeric&#39;, &#39;gender&#39;: &#39;binary&#39;, &#39;lab_platelet count&#39;: &#39;numeric&#39;, &#39;lab_mch&#39;: &#39;numeric&#39;, &#39;lab_potassium&#39;: &#39;numeric&#39;, &#39;age&#39;: &#39;numeric&#39;, &#39;lab_red blood cells&#39;: &#39;numeric&#39;, &#39;lab_ptt&#39;: &#39;numeric&#39;, &#39;lab_mcv&#39;: &#39;numeric&#39;, &#39;lab_rdw-sd&#39;: &#39;numeric&#39;, &#39;lab_i&#39;: &#39;numeric&#39;, &#39;admission_location&#39;: &#39;ordinal&#39;}
+{&#39;lab_mchc&#39;: &#39;numeric&#39;, &#39;lab_rdw&#39;: &#39;numeric&#39;, &#39;lab_asparate aminotransferase (ast)&#39;: &#39;numeric&#39;, &#39;lab_chloride&#39;: &#39;numeric&#39;, &#39;lab_creatinine&#39;: &#39;numeric&#39;, &#39;lab_platelet count&#39;: &#39;numeric&#39;, &#39;age&#39;: &#39;numeric&#39;, &#39;gender&#39;: &#39;binary&#39;, &#39;lab_mcv&#39;: &#39;numeric&#39;, &#39;lab_urea nitrogen&#39;: &#39;numeric&#39;, &#39;lab_magnesium&#39;: &#39;numeric&#39;, &#39;lab_ptt&#39;: &#39;numeric&#39;, &#39;lab_glucose&#39;: &#39;numeric&#39;, &#39;lab_ph&#39;: &#39;numeric&#39;, &#39;lab_rdw-sd&#39;: &#39;numeric&#39;, &#39;lab_alkaline phosphatase&#39;: &#39;numeric&#39;, &#39;lab_red blood cells&#39;: &#39;numeric&#39;, &#39;admission_location&#39;: &#39;ordinal&#39;, &#39;lab_pt&#39;: &#39;numeric&#39;, &#39;lab_bilirubin, total&#39;: &#39;numeric&#39;, &#39;lab_white blood cells&#39;: &#39;numeric&#39;, &#39;lab_h&#39;: &#39;numeric&#39;, &#39;lab_hemoglobin&#39;: &#39;numeric&#39;, &#39;lab_phosphate&#39;: &#39;numeric&#39;, &#39;lab_potassium&#39;: &#39;numeric&#39;, &#39;lab_bicarbonate&#39;: &#39;numeric&#39;, &#39;lab_hematocrit&#39;: &#39;numeric&#39;, &#39;lab_calcium, total&#39;: &#39;numeric&#39;, &#39;lab_anion gap&#39;: &#39;numeric&#39;, &#39;lab_i&#39;: &#39;numeric&#39;, &#39;lab_alanine aminotransferase (alt)&#39;: &#39;numeric&#39;, &#39;lab_l&#39;: &#39;numeric&#39;, &#39;lab_mch&#39;: &#39;numeric&#39;, &#39;mortality_outcome&#39;: &#39;binary&#39;, &#39;lab_sodium&#39;: &#39;numeric&#39;, &#39;lab_inr(pt)&#39;: &#39;numeric&#39;}
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@@ -1132,7 +1132,7 @@ <h2>Creating Hugging Face Dataset<a class="headerlink" href="#Creating-Hugging-F
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "6bb494a83e86428bba3e3cf765ba04b5"}</script></div>
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@@ -1205,7 +1205,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 11:59:59,281 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best reg_lambda: 10
+2024-06-23 18:01:51,540 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best reg_lambda: 0
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@@ -1213,7 +1213,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-22 11:59:59,284 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best n_estimators: 250
+2024-06-23 18:01:51,561 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best n_estimators: 500
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@@ -1221,7 +1221,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-22 11:59:59,285 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best max_depth: 5
+2024-06-23 18:01:51,564 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best max_depth: 5
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@@ -1229,7 +1229,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-22 11:59:59,287 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2024-06-23 18:01:51,566 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
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@@ -1237,7 +1237,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-22 11:59:59,288 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 1
+2024-06-23 18:01:51,568 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 2
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@@ -1245,7 +1245,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
 </div>
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-2024-06-22 11:59:59,289 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
+2024-06-23 18:01:51,569 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.7
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@@ -1658,26 +1658,26 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
   background-color: var(--sklearn-color-fitted-level-3);
 }
 </style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,
-              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,
-              early_stopping_rounds=None, enable_categorical=False,
-              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=1, gpu_id=None,
-              grow_policy=None, importance_type=None,
-              interaction_constraints=None, learning_rate=0.1, max_bin=None,
-              max_cat_threshold=None, max_cat_to_onehot=None,
-              max_delta_step=None, max_depth=5, max_leaves=None,
-              min_child_weight=3, missing=nan, monotone_constraints=None,
-              n_estimators=250, n_jobs=None, num_parallel_tree=None,
-              predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;XGBClassifier<span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,
-              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,
-              early_stopping_rounds=None, enable_categorical=False,
-              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=1, gpu_id=None,
-              grow_policy=None, importance_type=None,
-              interaction_constraints=None, learning_rate=0.1, max_bin=None,
-              max_cat_threshold=None, max_cat_to_onehot=None,
-              max_delta_step=None, max_depth=5, max_leaves=None,
-              min_child_weight=3, missing=nan, monotone_constraints=None,
-              n_estimators=250, n_jobs=None, num_parallel_tree=None,
-              predictor=None, random_state=123, ...)</pre></div> </div></div></div></div></div>
+              colsample_bylevel=None, colsample_bynode=None,
+              colsample_bytree=0.7, early_stopping_rounds=None,
+              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,
+              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,
+              importance_type=None, interaction_constraints=None,
+              learning_rate=0.1, max_bin=None, max_cat_threshold=None,
+              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,
+              max_leaves=None, min_child_weight=3, missing=nan,
+              monotone_constraints=None, n_estimators=500, n_jobs=None,
+              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;XGBClassifier<span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,
+              colsample_bylevel=None, colsample_bynode=None,
+              colsample_bytree=0.7, early_stopping_rounds=None,
+              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,
+              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,
+              importance_type=None, interaction_constraints=None,
+              learning_rate=0.1, max_bin=None, max_cat_threshold=None,
+              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,
+              max_leaves=None, min_child_weight=3, missing=nan,
+              monotone_constraints=None, n_estimators=500, n_jobs=None,
+              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre></div> </div></div></div></div></div>
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@@ -1693,7 +1693,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-{&#39;objective&#39;: &#39;binary:logistic&#39;, &#39;use_label_encoder&#39;: None, &#39;base_score&#39;: None, &#39;booster&#39;: None, &#39;callbacks&#39;: None, &#39;colsample_bylevel&#39;: None, &#39;colsample_bynode&#39;: None, &#39;colsample_bytree&#39;: 1, &#39;early_stopping_rounds&#39;: None, &#39;enable_categorical&#39;: False, &#39;eval_metric&#39;: &#39;logloss&#39;, &#39;feature_types&#39;: None, &#39;gamma&#39;: 1, &#39;gpu_id&#39;: None, &#39;grow_policy&#39;: None, &#39;importance_type&#39;: None, &#39;interaction_constraints&#39;: None, &#39;learning_rate&#39;: 0.1, &#39;max_bin&#39;: None, &#39;max_cat_threshold&#39;: None, &#39;max_cat_to_onehot&#39;: None, &#39;max_delta_step&#39;: None, &#39;max_depth&#39;: 5, &#39;max_leaves&#39;: None, &#39;min_child_weight&#39;: 3, &#39;missing&#39;: nan, &#39;monotone_constraints&#39;: None, &#39;n_estimators&#39;: 250, &#39;n_jobs&#39;: None, &#39;num_parallel_tree&#39;: None, &#39;predictor&#39;: None, &#39;random_state&#39;: 123, &#39;reg_alpha&#39;: None, &#39;reg_lambda&#39;: 10, &#39;sampling_method&#39;: None, &#39;scale_pos_weight&#39;: None, &#39;subsample&#39;: None, &#39;tree_method&#39;: None, &#39;validate_parameters&#39;: None, &#39;verbosity&#39;: None, &#39;seed&#39;: 123}
+{&#39;objective&#39;: &#39;binary:logistic&#39;, &#39;use_label_encoder&#39;: None, &#39;base_score&#39;: None, &#39;booster&#39;: None, &#39;callbacks&#39;: None, &#39;colsample_bylevel&#39;: None, &#39;colsample_bynode&#39;: None, &#39;colsample_bytree&#39;: 0.7, &#39;early_stopping_rounds&#39;: None, &#39;enable_categorical&#39;: False, &#39;eval_metric&#39;: &#39;logloss&#39;, &#39;feature_types&#39;: None, &#39;gamma&#39;: 2, &#39;gpu_id&#39;: None, &#39;grow_policy&#39;: None, &#39;importance_type&#39;: None, &#39;interaction_constraints&#39;: None, &#39;learning_rate&#39;: 0.1, &#39;max_bin&#39;: None, &#39;max_cat_threshold&#39;: None, &#39;max_cat_to_onehot&#39;: None, &#39;max_delta_step&#39;: None, &#39;max_depth&#39;: 5, &#39;max_leaves&#39;: None, &#39;min_child_weight&#39;: 3, &#39;missing&#39;: nan, &#39;monotone_constraints&#39;: None, &#39;n_estimators&#39;: 500, &#39;n_jobs&#39;: None, &#39;num_parallel_tree&#39;: None, &#39;predictor&#39;: None, &#39;random_state&#39;: 123, &#39;reg_alpha&#39;: None, &#39;reg_lambda&#39;: 0, &#39;sampling_method&#39;: None, &#39;scale_pos_weight&#39;: None, &#39;subsample&#39;: None, &#39;tree_method&#39;: None, &#39;validate_parameters&#39;: None, &#39;verbosity&#39;: None, &#39;seed&#39;: 123}
 </pre></div></div>
 </div>
 <p><strong>Log the model parameters to the report.</strong></p>
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 <p><strong>Log the performance metrics to the report.</strong></p>
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diff --git a/api/tutorials/mimiciv/mortality_prediction.ipynb b/api/tutorials/mimiciv/mortality_prediction.ipynb
index 4435e5438..7475727f0 100644
--- a/api/tutorials/mimiciv/mortality_prediction.ipynb
+++ b/api/tutorials/mimiciv/mortality_prediction.ipynb
@@ -21,10 +21,10 @@
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-     "shell.execute_reply": "2024-06-22T15:27:41.439890Z"
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@@ -89,10 +89,10 @@
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-     "shell.execute_reply": "2024-06-22T15:27:41.457459Z"
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@@ -112,10 +112,10 @@
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@@ -147,10 +147,10 @@
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@@ -158,21 +158,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:27:44,063 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Database setup, ready to run queries!\n"
+      "2024-06-23 17:51:40,599 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Database setup, ready to run queries!\n"
      ]
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     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:28:05,493 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
+      "2024-06-23 17:51:59,829 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:28:05,496 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 19.191247 s\n"
+      "2024-06-23 17:51:59,831 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 16.893647 s\n"
      ]
     },
     {
@@ -196,14 +196,14 @@
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      "output_type": "stream",
      "text": [
-      "2024-06-22 11:30:49,671 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
+      "2024-06-23 17:54:34,379 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
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      "output_type": "stream",
      "text": [
-      "2024-06-22 11:30:49,673 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 161.002521 s\n"
+      "2024-06-23 17:54:34,382 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 151.679064 s\n"
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@@ -406,10 +406,10 @@
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@@ -2407,9 +2407,9 @@
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@@ -2467,10 +2467,10 @@
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@@ -2488,10 +2488,10 @@
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@@ -48152,10 +48152,10 @@
    "execution_count": 14,
    "metadata": {
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-     "iopub.execute_input": "2024-06-22T15:34:40.865394Z",
-     "iopub.status.busy": "2024-06-22T15:34:40.864739Z",
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-     "shell.execute_reply": "2024-06-22T15:34:40.882607Z"
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@@ -48181,10 +48181,10 @@
    "execution_count": 15,
    "metadata": {
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-     "iopub.execute_input": "2024-06-22T15:34:40.891193Z",
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@@ -48192,7 +48192,7 @@
      "name": "stdout",
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      "text": [
-      "{'lab_calcium, total': 'numeric', 'lab_alkaline phosphatase': 'numeric', 'lab_hematocrit': 'numeric', 'lab_chloride': 'numeric', 'lab_hemoglobin': 'numeric', 'lab_inr(pt)': 'numeric', 'mortality_outcome': 'binary', 'lab_pt': 'numeric', 'lab_l': 'numeric', 'lab_phosphate': 'numeric', 'lab_urea nitrogen': 'numeric', 'lab_alanine aminotransferase (alt)': 'numeric', 'lab_h': 'numeric', 'lab_bicarbonate': 'numeric', 'lab_mchc': 'numeric', 'lab_white blood cells': 'numeric', 'lab_creatinine': 'numeric', 'lab_rdw': 'numeric', 'lab_glucose': 'numeric', 'lab_bilirubin, total': 'numeric', 'lab_anion gap': 'numeric', 'lab_asparate aminotransferase (ast)': 'numeric', 'lab_ph': 'numeric', 'lab_magnesium': 'numeric', 'lab_sodium': 'numeric', 'gender': 'binary', 'lab_platelet count': 'numeric', 'lab_mch': 'numeric', 'lab_potassium': 'numeric', 'age': 'numeric', 'lab_red blood cells': 'numeric', 'lab_ptt': 'numeric', 'lab_mcv': 'numeric', 'lab_rdw-sd': 'numeric', 'lab_i': 'numeric', 'admission_location': 'ordinal'}\n"
+      "{'lab_mchc': 'numeric', 'lab_rdw': 'numeric', 'lab_asparate aminotransferase (ast)': 'numeric', 'lab_chloride': 'numeric', 'lab_creatinine': 'numeric', 'lab_platelet count': 'numeric', 'age': 'numeric', 'gender': 'binary', 'lab_mcv': 'numeric', 'lab_urea nitrogen': 'numeric', 'lab_magnesium': 'numeric', 'lab_ptt': 'numeric', 'lab_glucose': 'numeric', 'lab_ph': 'numeric', 'lab_rdw-sd': 'numeric', 'lab_alkaline phosphatase': 'numeric', 'lab_red blood cells': 'numeric', 'admission_location': 'ordinal', 'lab_pt': 'numeric', 'lab_bilirubin, total': 'numeric', 'lab_white blood cells': 'numeric', 'lab_h': 'numeric', 'lab_hemoglobin': 'numeric', 'lab_phosphate': 'numeric', 'lab_potassium': 'numeric', 'lab_bicarbonate': 'numeric', 'lab_hematocrit': 'numeric', 'lab_calcium, total': 'numeric', 'lab_anion gap': 'numeric', 'lab_i': 'numeric', 'lab_alanine aminotransferase (alt)': 'numeric', 'lab_l': 'numeric', 'lab_mch': 'numeric', 'mortality_outcome': 'binary', 'lab_sodium': 'numeric', 'lab_inr(pt)': 'numeric'}\n"
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@@ -48222,10 +48222,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T15:34:41.220815Z",
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-     "iopub.status.idle": "2024-06-22T15:34:41.229102Z",
-     "shell.execute_reply": "2024-06-22T15:34:41.227469Z"
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@@ -48243,10 +48243,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T15:34:41.235972Z",
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-     "shell.execute_reply": "2024-06-22T15:34:41.340223Z"
+     "iopub.execute_input": "2024-06-23T21:58:24.426918Z",
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@@ -48271,10 +48271,10 @@
    "execution_count": 18,
    "metadata": {
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-     "shell.execute_reply": "2024-06-22T15:34:41.367108Z"
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@@ -48304,10 +48304,10 @@
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    "metadata": {
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-     "iopub.status.idle": "2024-06-22T15:34:41.385803Z",
-     "shell.execute_reply": "2024-06-22T15:34:41.384218Z"
+     "iopub.execute_input": "2024-06-23T21:58:24.564528Z",
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@@ -48344,10 +48344,10 @@
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    "metadata": {
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-     "shell.execute_reply": "2024-06-22T15:34:41.660922Z"
+     "iopub.execute_input": "2024-06-23T21:58:24.581106Z",
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@@ -48373,17 +48373,17 @@
    "execution_count": 21,
    "metadata": {
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-     "iopub.status.busy": "2024-06-22T15:34:41.669335Z",
-     "iopub.status.idle": "2024-06-22T15:34:41.889819Z",
-     "shell.execute_reply": "2024-06-22T15:34:41.888146Z"
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+     "iopub.status.busy": "2024-06-23T21:58:24.857356Z",
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+     "shell.execute_reply": "2024-06-23T21:58:25.079459Z"
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     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "37007f1e7a9143f1b1efb27bb2c7d843",
+       "model_id": "6bb494a83e86428bba3e3cf765ba04b5",
        "version_major": 2,
        "version_minor": 0
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@@ -48418,10 +48418,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T15:34:41.897812Z",
-     "iopub.status.busy": "2024-06-22T15:34:41.896268Z",
-     "iopub.status.idle": "2024-06-22T15:34:45.199665Z",
-     "shell.execute_reply": "2024-06-22T15:34:45.197895Z"
+     "iopub.execute_input": "2024-06-23T21:58:25.089750Z",
+     "iopub.status.busy": "2024-06-23T21:58:25.088342Z",
+     "iopub.status.idle": "2024-06-23T21:58:28.113248Z",
+     "shell.execute_reply": "2024-06-23T21:58:28.111383Z"
     }
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@@ -48444,10 +48444,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T15:34:45.207814Z",
-     "iopub.status.busy": "2024-06-22T15:34:45.206433Z",
-     "iopub.status.idle": "2024-06-22T15:34:45.218331Z",
-     "shell.execute_reply": "2024-06-22T15:34:45.216776Z"
+     "iopub.execute_input": "2024-06-23T21:58:28.121792Z",
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+     "shell.execute_reply": "2024-06-23T21:58:28.131002Z"
     }
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@@ -48487,10 +48487,10 @@
    "execution_count": 24,
    "metadata": {
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-     "iopub.execute_input": "2024-06-22T15:34:45.225082Z",
-     "iopub.status.busy": "2024-06-22T15:34:45.224515Z",
-     "iopub.status.idle": "2024-06-22T15:59:59.353687Z",
-     "shell.execute_reply": "2024-06-22T15:59:59.351668Z"
+     "iopub.execute_input": "2024-06-23T21:58:28.139826Z",
+     "iopub.status.busy": "2024-06-23T21:58:28.138774Z",
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@@ -48498,42 +48498,42 @@
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      "output_type": "stream",
      "text": [
-      "2024-06-22 11:59:59,281 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 10\n"
+      "2024-06-23 18:01:51,540 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:59:59,284 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 250\n"
+      "2024-06-23 18:01:51,561 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 500\n"
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      "text": [
-      "2024-06-22 11:59:59,285 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
+      "2024-06-23 18:01:51,564 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:59:59,287 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
+      "2024-06-23 18:01:51,566 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:59:59,288 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 1\n"
+      "2024-06-23 18:01:51,568 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 2\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 11:59:59,289 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 1\n"
+      "2024-06-23 18:01:51,569 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.7\n"
      ]
     },
     {
@@ -48944,39 +48944,39 @@
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,\n",
-       "              early_stopping_rounds=None, enable_categorical=False,\n",
-       "              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=1, gpu_id=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
-       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=250, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,\n",
-       "              early_stopping_rounds=None, enable_categorical=False,\n",
-       "              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=1, gpu_id=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
-       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=250, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=123, ...)</pre></div> </div></div></div></div>"
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=0.7, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
+       "              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=500, n_jobs=None,\n",
+       "              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=0.7, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
+       "              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=500, n_jobs=None,\n",
+       "              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,\n",
-       "              early_stopping_rounds=None, enable_categorical=False,\n",
-       "              eval_metric='logloss', feature_types=None, gamma=1, gpu_id=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
-       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=250, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=123, ...)"
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=0.7, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric='logloss',\n",
+       "              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=500, n_jobs=None,\n",
+       "              num_parallel_tree=None, predictor=None, random_state=123, ...)"
       ]
      },
      "execution_count": 24,
@@ -49007,10 +49007,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T15:59:59.362366Z",
-     "iopub.status.busy": "2024-06-22T15:59:59.361530Z",
-     "iopub.status.idle": "2024-06-22T15:59:59.372886Z",
-     "shell.execute_reply": "2024-06-22T15:59:59.371170Z"
+     "iopub.execute_input": "2024-06-23T22:01:51.716240Z",
+     "iopub.status.busy": "2024-06-23T22:01:51.714101Z",
+     "iopub.status.idle": "2024-06-23T22:01:51.731453Z",
+     "shell.execute_reply": "2024-06-23T22:01:51.729792Z"
     }
    },
    "outputs": [
@@ -49018,7 +49018,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 1, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 250, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 10, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}\n"
+      "{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.7, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}\n"
      ]
     }
    ],
@@ -49041,10 +49041,10 @@
    "execution_count": 26,
    "metadata": {
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-     "iopub.execute_input": "2024-06-22T15:59:59.380586Z",
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-     "iopub.status.idle": "2024-06-22T15:59:59.389227Z",
-     "shell.execute_reply": "2024-06-22T15:59:59.387523Z"
+     "iopub.execute_input": "2024-06-23T22:01:51.738453Z",
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+     "iopub.status.idle": "2024-06-23T22:01:51.752867Z",
+     "shell.execute_reply": "2024-06-23T22:01:51.751240Z"
     }
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    "outputs": [],
@@ -49066,17 +49066,17 @@
    "execution_count": 27,
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-     "iopub.execute_input": "2024-06-22T15:59:59.396931Z",
-     "iopub.status.busy": "2024-06-22T15:59:59.395268Z",
-     "iopub.status.idle": "2024-06-22T16:00:02.439909Z",
-     "shell.execute_reply": "2024-06-22T16:00:02.437895Z"
+     "iopub.execute_input": "2024-06-23T22:01:51.759840Z",
+     "iopub.status.busy": "2024-06-23T22:01:51.758753Z",
+     "iopub.status.idle": "2024-06-23T22:01:54.846053Z",
+     "shell.execute_reply": "2024-06-23T22:01:54.842920Z"
     }
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    "outputs": [
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        "version_major": 2,
        "version_minor": 0
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@@ -49122,10 +49122,10 @@
    "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T16:00:02.447476Z",
-     "iopub.status.busy": "2024-06-22T16:00:02.446841Z",
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-     "shell.execute_reply": "2024-06-22T16:00:02.484704Z"
+     "iopub.execute_input": "2024-06-23T22:01:54.854127Z",
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@@ -49158,10 +49158,10 @@
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-     "shell.execute_reply": "2024-06-22T16:00:02.504453Z"
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     }
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@@ -49201,10 +49201,10 @@
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-     "shell.execute_reply": "2024-06-22T16:00:02.531373Z"
+     "iopub.execute_input": "2024-06-23T22:01:54.988787Z",
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@@ -49235,10 +49235,10 @@
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-     "shell.execute_reply": "2024-06-22T16:00:02.547949Z"
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@@ -49266,17 +49266,17 @@
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-     "shell.execute_reply": "2024-06-22T16:00:10.225479Z"
+     "iopub.execute_input": "2024-06-23T22:01:55.038101Z",
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     }
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@@ -49290,7 +49290,7 @@
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@@ -49304,7 +49304,7 @@
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@@ -49318,7 +49318,7 @@
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@@ -49332,7 +49332,7 @@
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@@ -49346,7 +49346,7 @@
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@@ -49360,7 +49360,7 @@
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@@ -49374,7 +49374,7 @@
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@@ -49388,7 +49388,7 @@
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@@ -49402,7 +49402,7 @@
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@@ -49416,7 +49416,7 @@
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@@ -49430,7 +49430,7 @@
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@@ -49444,7 +49444,7 @@
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@@ -49458,7 +49458,7 @@
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@@ -49472,7 +49472,7 @@
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@@ -49514,10 +49514,10 @@
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-    "# 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()"
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+       "                            \n",
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+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
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+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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": 39,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-23T22:02:03.354278Z",
+     "iopub.status.busy": "2024-06-23T22:02:03.352611Z",
+     "iopub.status.idle": "2024-06-23T22:02:03.521278Z",
+     "shell.execute_reply": "2024-06-23T22:02:03.519512Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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@@ -83362,10 +83338,10 @@
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@@ -83387,12 +83363,12 @@
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@@ -84229,9 +84205,9 @@
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@@ -84279,10 +84255,10 @@
    "execution_count": 42,
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-     "iopub.status.idle": "2024-06-22T16:00:11.058079Z",
-     "shell.execute_reply": "2024-06-22T16:00:11.056448Z"
+     "iopub.execute_input": "2024-06-23T22:02:03.655457Z",
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+     "shell.execute_reply": "2024-06-23T22:02:03.662072Z"
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@@ -84303,10 +84279,10 @@
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-        <span style="font-size: 48px; font-weight:bolder; color: green">0.85</span>
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 </div>
 
@@ -1526,7 +1526,7 @@ <h3>Version</h3>
         
           
             
-              <b>Date:</b> 2024-06-22
+              <b>Date:</b> 2024-06-23
             
           
           <br>
@@ -1745,8 +1745,8 @@ <h2 class="collapsible">Model Parameters</h2>
   
     
     <div class="subcard">
-      <h3>Reg_lambda</h3>
-      10
+      <h3>Objective</h3>
+      binary:logistic
     </div>
     
   
@@ -1763,6 +1763,10 @@ <h3>Reg_lambda</h3>
 
   
     
+    <div class="subcard">
+      <h3>Max_depth</h3>
+      5
+    </div>
     
   
 
@@ -1773,15 +1777,15 @@ <h3>Reg_lambda</h3>
 
   
     
+    <div class="subcard">
+      <h3>Eval_metric</h3>
+      logloss
+    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Missing</h3>
-      nan
-    </div>
     
   
 
@@ -1807,10 +1811,6 @@ <h3>Missing</h3>
 
   
     
-    <div class="subcard">
-      <h3>Seed</h3>
-      123
-    </div>
     
   
 
@@ -1826,6 +1826,10 @@ <h3>Seed</h3>
 
   
     
+    <div class="subcard">
+      <h3>Random_state</h3>
+      123
+    </div>
     
   
 
@@ -1841,11 +1845,19 @@ <h3>Seed</h3>
 
   
     
+    <div class="subcard">
+      <h3>Seed</h3>
+      123
+    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Gamma</h3>
+      2
+    </div>
     
   
 
@@ -1856,10 +1868,6 @@ <h3>Seed</h3>
 
   
     
-    <div class="subcard">
-      <h3>N_estimators</h3>
-      250
-    </div>
     
   
 
@@ -1870,6 +1878,10 @@ <h3>N_estimators</h3>
 
   
     
+    <div class="subcard">
+      <h3>Reg_lambda</h3>
+      0
+    </div>
     
   
 
@@ -1880,19 +1892,11 @@ <h3>N_estimators</h3>
 
   
     
-    <div class="subcard">
-      <h3>Learning_rate</h3>
-      0.1
-    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Random_state</h3>
-      123
-    </div>
     
   
 
@@ -1903,15 +1907,15 @@ <h3>Random_state</h3>
 
   
     
-    <div class="subcard">
-      <h3>Enable_categorical</h3>
-      False
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Enable_categorical</h3>
+      False
+    </div>
     
   
 
@@ -1927,8 +1931,8 @@ <h3>Min_child_weight</h3>
   
     
     <div class="subcard">
-      <h3>Max_depth</h3>
-      5
+      <h3>Learning_rate</h3>
+      0.1
     </div>
     
   
@@ -1945,10 +1949,6 @@ <h3>Max_depth</h3>
 
   
     
-    <div class="subcard">
-      <h3>Eval_metric</h3>
-      logloss
-    </div>
     
   
 
@@ -1960,8 +1960,8 @@ <h3>Eval_metric</h3>
   
     
     <div class="subcard">
-      <h3>Gamma</h3>
-      1
+      <h3>N_estimators</h3>
+      500
     </div>
     
   
@@ -1974,23 +1974,23 @@ <h3>Gamma</h3>
   
     
     <div class="subcard">
-      <h3>Objective</h3>
-      binary:logistic
+      <h3>Missing</h3>
+      nan
     </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Colsample_bytree</h3>
-      1
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Colsample_bytree</h3>
+      0.7
+    </div>
     
   
 
@@ -2365,9 +2365,9 @@ <h3>Ethical Considerations</h3>
       }
   }
   var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:AveragePrecision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"6\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"11\": [\"metric:AveragePrecision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"12\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"16\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"17\": [\"metric:AveragePrecision\", \"gender:M\", \"age:overall_age\"], \"18\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"21\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"22\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"23\": [\"metric:AveragePrecision\", \"gender:F\", \"age:overall_age\"], \"24\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"25\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"26\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"27\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"28\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"], \"29\": [\"metric:AveragePrecision\", \"age:overall_age\", \"gender:overall_gender\"]}");
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+  var histories_all = JSON.parse("{\"0\": [\"0.9927007555961609\", \"0.9084577556659906\", \"0.9967123505250632\", \"0.9361637171500369\", \"0.7970945087177168\"], \"1\": [\"0.5\", \"0.5871161364713195\", \"0.6427614945669572\", \"0.7123207351031206\", \"0.7693962240641155\"], \"2\": [\"0.20000000298023224\", \"0.1250038868989642\", \"0.03259395329045932\", \"0.03319717901877138\", \"0.0\"], \"3\": [\"0.2857142984867096\", \"0.15099968687132234\", \"0.31955516966378655\", \"0.2078074856118015\", \"0.34403908703582164\"], \"4\": [\"0.8376470804214478\", \"0.851705052986469\", \"0.8549064019158588\", \"0.8715990099807263\", \"0.9083766863936913\"], \"5\": [\"0.20127536356449127\", \"0.22730227860628935\", \"0.22650506425281836\", \"0.1479281472634238\", \"0.3049563375425116\"], \"6\": [\"0.9754148721694946\", \"0.8693023091304493\", \"0.8829761838493525\", \"0.852504229229007\", \"0.8256869937362297\"], \"7\": [\"0.3478260934352875\", \"0.2719131707443655\", \"0.009444526819723409\", \"0.0\", \"0.0\"], \"8\": [\"0.24242424964904785\", \"0.3761319090848231\", \"0.43667014852713304\", \"0.4866882347011495\", \"0.4174179040861352\"], \"9\": [\"0.2857142984867096\", \"0.24497241222728497\", \"0.20308995383515835\", \"0.3301458050299214\", \"0.1531067913292969\"], \"10\": [\"0.8921904563903809\", \"0.8553745921639088\", \"0.7622205918111564\", \"0.8144962759724492\", \"0.9053699954080255\"], \"11\": [\"0.255609393119812\", \"0.20684319820018238\", \"0.2124718685109644\", \"0.17035875481360624\", \"0.19798325421505883\"], \"12\": [\"0.9728915691375732\", \"1.0\", \"1.0\", \"0.9795997167410667\", \"1.0\"], \"13\": [\"0.2800000011920929\", \"0.2582725874984817\", \"0.37238248588781087\", \"0.2435939528636595\", \"0.23137984171780207\"], \"14\": [\"0.2800000011920929\", \"0.37120425956456843\", \"0.29522356360055857\", \"0.21291825287340976\", \"0.3167588325082886\"], \"15\": [\"0.2800000011920929\", \"0.1854585053638812\", \"0.1739141294195912\", \"0.08457587414417689\", \"0.09803278069196336\"], \"16\": [\"0.8564236760139465\", \"0.9211616658239623\", \"0.8161455066742371\", \"0.7692510919701953\", \"0.9768927991487647\"], \"17\": [\"0.18138155341148376\", \"0.3836534063808792\", \"0.32758153272326956\", \"0.4113266996701133\", \"0.570285361295733\"], \"18\": [\"0.9790164232254028\", \"1.0\", \"1.0\", \"1.0\", \"0.964453883754515\"], \"19\": [\"0.3499999940395355\", \"0.3863954430145741\", \"0.24755652007061535\", \"0.5051159855045335\", \"0.49375728106831906\"], \"20\": [\"0.26923078298568726\", \"0.3038250697216256\", \"0.25850919457814997\", \"0.2518948811458642\", \"0.4459606481557118\"], \"21\": [\"0.30434781312942505\", \"0.23824684326423057\", \"0.20321435109957497\", \"0.2935219811348861\", \"0.31346873379591406\"], \"22\": [\"0.9008569717407227\", \"1.0\", \"1.0\", \"0.9140278427225832\", \"1.0\"], \"23\": [\"0.28123924136161804\", \"0.3762084986412605\", \"0.3306538281125844\", \"0.24729012034954972\", \"0.30802978646595547\"], \"24\": [\"0.9761654138565063\", \"1.0\", \"0.9389906906786338\", \"0.8338169486377209\", \"0.9372759027305281\"], \"25\": [\"0.31111112236976624\", \"0.3208377413980842\", \"0.3948946957047839\", \"0.31539736452749256\", \"0.3728005225544868\"], \"26\": [\"0.27450981736183167\", \"0.27961555028769025\", \"0.2999792434857541\", \"0.31526039999962985\", \"0.27444905495904626\"], \"27\": [\"0.2916666567325592\", \"0.25051975408216337\", \"0.31948167829640195\", \"0.29195289901624977\", \"0.19997201586106395\"], \"28\": [\"0.8802536129951477\", \"0.8857792893282364\", \"0.9536138581831235\", \"1.0\", \"0.9655741864371827\"], \"29\": [\"0.21615521609783173\", \"0.24735582070032103\", \"0.20091912397467232\", \"0.30567041309612497\", \"0.4337665892419137\"]}");
   var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\", \"25\": \"0.7\", \"26\": \"0.7\", \"27\": \"0.7\", \"28\": \"0.7\", \"29\": \"0.7\"}");
-  var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": false, \"3\": false, \"4\": true, \"5\": false, \"6\": false, \"7\": false, \"8\": false, \"9\": false, \"10\": true, \"11\": false, \"12\": true, \"13\": false, \"14\": false, \"15\": false, \"16\": true, \"17\": false, \"18\": false, \"19\": false, \"20\": false, \"21\": false, \"22\": true, \"23\": false, \"24\": true, \"25\": false, \"26\": false, \"27\": false, \"28\": true, \"29\": false}");
+  var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": false, \"3\": false, \"4\": true, \"5\": false, \"6\": true, \"7\": false, \"8\": false, \"9\": false, \"10\": true, \"11\": false, \"12\": true, \"13\": false, \"14\": false, \"15\": false, \"16\": true, \"17\": false, \"18\": true, \"19\": false, \"20\": false, \"21\": false, \"22\": true, \"23\": false, \"24\": true, \"25\": false, \"26\": false, \"27\": false, \"28\": true, \"29\": false}");
   var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"AveragePrecision\", \"6\": \"Accuracy\", \"7\": \"Precision\", \"8\": \"Recall\", \"9\": \"F1 Score\", \"10\": \"AUROC\", \"11\": \"AveragePrecision\", \"12\": \"Accuracy\", \"13\": \"Precision\", \"14\": \"Recall\", \"15\": \"F1 Score\", \"16\": \"AUROC\", \"17\": \"AveragePrecision\", \"18\": \"Accuracy\", \"19\": \"Precision\", \"20\": \"Recall\", \"21\": \"F1 Score\", \"22\": \"AUROC\", \"23\": \"AveragePrecision\", \"24\": \"Accuracy\", \"25\": \"Precision\", \"26\": \"Recall\", \"27\": \"F1 Score\", \"28\": \"AUROC\", \"29\": \"AveragePrecision\"}");
   var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"25\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"26\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"27\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"28\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"29\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
 
@@ -2669,7 +2669,7 @@ <h3>Ethical Considerations</h3>
 function generate_model_card_plot() {
   var model_card_plots = []
   var overall_indices = [24, 25, 26, 27, 28, 29]
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   var thresholds = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\", \"25\": \"0.7\", \"26\": \"0.7\", \"27\": \"0.7\", \"28\": \"0.7\", \"29\": \"0.7\"}");
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@@ -2969,9 +2969,9 @@ <h3>Ethical Considerations</h3>
       }
   }
   var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:AveragePrecision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"6\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"11\": [\"metric:AveragePrecision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"12\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"16\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"17\": [\"metric:AveragePrecision\", \"gender:M\", \"age:overall_age\"], \"18\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"21\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"22\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"23\": [\"metric:AveragePrecision\", \"gender:F\", \"age:overall_age\"], \"24\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"25\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"26\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"27\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"28\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"], \"29\": [\"metric:AveragePrecision\", \"age:overall_age\", \"gender:overall_gender\"]}");
-  var histories_all = JSON.parse("{\"0\": [\"0.9955621361732483\", \"1.0\", \"0.9090785186092738\", \"0.9335863596165629\", \"0.8186038363208302\"], \"1\": [\"1.0\", \"0.8456962509602705\", \"0.9355320907624267\", \"1.0\", \"0.9236438945572603\"], \"2\": [\"0.25\", \"0.30175867417868596\", \"0.4090271995112156\", \"0.642184940598215\", \"0.5625667826893166\"], \"3\": [\"0.4000000059604645\", \"0.469665571695541\", \"0.46555891294035706\", \"0.4246929861436543\", \"0.4764817145865098\"], \"4\": [\"0.9386160373687744\", \"0.9080135439423599\", \"0.718799557764541\", \"0.7718448217725096\", \"0.8908813868319999\"], \"5\": [\"0.3177526295185089\", \"0.4335309481655152\", \"0.4807214483612478\", \"0.4444917218015315\", \"0.5549339173062351\"], \"6\": [\"0.9685573577880859\", \"1.0\", \"0.8178219968390712\", \"0.7003549365904126\", \"0.6003240792292783\"], \"7\": [\"0.30000001192092896\", \"0.35909826175864534\", \"0.47348944179776753\", \"0.5569090961800103\", \"0.6604832940331692\"], \"8\": [\"0.34285715222358704\", \"0.37150638217185034\", \"0.2844859307436997\", \"0.27537346422318676\", \"0.37507420184202916\"], \"9\": [\"0.3199999928474426\", \"0.49736885117328794\", \"0.5623958932500298\", \"0.5664318809645187\", \"0.5967372035678011\"], \"10\": [\"0.8876226544380188\", \"0.8737110864533941\", \"1.0\", \"1.0\", \"1.0\"], \"11\": [\"0.2625003457069397\", \"0.2108439609683797\", \"0.0856302249586883\", \"0.0\", \"0.029420203307738438\"], \"12\": [\"0.9683908224105835\", \"0.9497491944262126\", \"0.9844978426087068\", \"1.0\", \"1.0\"], \"13\": [\"0.25\", \"0.3762954716415773\", \"0.45456463582296663\", \"0.5339118438393122\", \"0.5069639029619843\"], \"14\": [\"0.3461538553237915\", \"0.4947142590995716\", \"0.6704204325189426\", \"0.43423251643343996\", \"0.33013811548328653\"], \"15\": [\"0.29032257199287415\", \"0.35936163576848845\", \"0.2716241704399406\", \"0.2873628724031001\", \"0.38412138680037927\"], \"16\": [\"0.8653283715248108\", \"0.7291528249452761\", \"0.7700926939846393\", \"0.8493640536679986\", \"0.8657139349432235\"], \"17\": [\"0.27834367752075195\", \"0.15908432828490746\", \"0.09953745722591939\", \"0.05865593901864767\", \"0.06450930931244318\"], \"18\": [\"0.9780972003936768\", \"0.7810265759607673\", \"0.6391780088514672\", \"0.4022863555958669\", \"0.3127863936260465\"], \"19\": [\"0.36000001430511475\", \"0.3972208850236971\", \"0.3631355792628282\", \"0.27886805399935\", \"0.1472883753069758\"], \"20\": [\"0.36000001430511475\", \"0.3463096235651758\", \"0.3282687398531431\", \"0.1909615411077286\", \"0.3170350839086391\"], \"21\": [\"0.36000001430511475\", \"0.37975933594401634\", \"0.3595360205520707\", \"0.33289765378650693\", \"0.33517848204054623\"], \"22\": [\"0.9137047529220581\", \"0.7762175331349029\", \"0.9065865605338307\", \"0.8949404254956155\", \"1.0\"], \"23\": [\"0.2226799726486206\", \"0.23475933338139618\", \"0.30145735120000094\", \"0.4760373158972946\", \"0.5733008455410238\"], \"24\": [\"0.9733613729476929\", \"1.0\", \"1.0\", \"1.0\", \"0.9458611099827602\"], \"25\": [\"0.2950819730758667\", \"0.17918503949614295\", \"0.16078404851913272\", \"0.2286724166660674\", \"0.33070381701707174\"], \"26\": [\"0.3529411852359772\", \"0.3284656940326658\", \"0.09462185151495309\", \"0.01763941619196635\", \"0.17240288369907394\"], \"27\": [\"0.3214285671710968\", \"0.36647245688435953\", \"0.4894612008120508\", \"0.2919148118470184\", \"0.16720726225318921\"], \"28\": [\"0.8884620666503906\", \"0.9653942632935267\", \"0.9927010081623271\", \"0.9495828470650614\", \"0.8467377469882281\"], \"29\": [\"0.2392815202474594\", \"0.20562696816732118\", \"0.2705518602353964\", \"0.18884185073779158\", \"0.0557719953685491\"]}");
+  var histories_all = JSON.parse("{\"0\": [\"0.9927007555961609\", \"0.9084577556659906\", \"0.9967123505250632\", \"0.9361637171500369\", \"0.7970945087177168\"], \"1\": [\"0.5\", \"0.5871161364713195\", \"0.6427614945669572\", \"0.7123207351031206\", \"0.7693962240641155\"], \"2\": [\"0.20000000298023224\", \"0.1250038868989642\", \"0.03259395329045932\", \"0.03319717901877138\", \"0.0\"], \"3\": [\"0.2857142984867096\", \"0.15099968687132234\", \"0.31955516966378655\", \"0.2078074856118015\", \"0.34403908703582164\"], \"4\": [\"0.8376470804214478\", \"0.851705052986469\", \"0.8549064019158588\", \"0.8715990099807263\", \"0.9083766863936913\"], \"5\": [\"0.20127536356449127\", \"0.22730227860628935\", \"0.22650506425281836\", \"0.1479281472634238\", \"0.3049563375425116\"], \"6\": [\"0.9754148721694946\", \"0.8693023091304493\", \"0.8829761838493525\", \"0.852504229229007\", \"0.8256869937362297\"], \"7\": [\"0.3478260934352875\", \"0.2719131707443655\", \"0.009444526819723409\", \"0.0\", \"0.0\"], \"8\": [\"0.24242424964904785\", \"0.3761319090848231\", \"0.43667014852713304\", \"0.4866882347011495\", \"0.4174179040861352\"], \"9\": [\"0.2857142984867096\", \"0.24497241222728497\", \"0.20308995383515835\", \"0.3301458050299214\", \"0.1531067913292969\"], \"10\": [\"0.8921904563903809\", \"0.8553745921639088\", \"0.7622205918111564\", \"0.8144962759724492\", \"0.9053699954080255\"], \"11\": [\"0.255609393119812\", \"0.20684319820018238\", \"0.2124718685109644\", \"0.17035875481360624\", \"0.19798325421505883\"], \"12\": [\"0.9728915691375732\", \"1.0\", \"1.0\", \"0.9795997167410667\", \"1.0\"], \"13\": [\"0.2800000011920929\", \"0.2582725874984817\", \"0.37238248588781087\", \"0.2435939528636595\", \"0.23137984171780207\"], \"14\": [\"0.2800000011920929\", \"0.37120425956456843\", \"0.29522356360055857\", \"0.21291825287340976\", \"0.3167588325082886\"], \"15\": [\"0.2800000011920929\", \"0.1854585053638812\", \"0.1739141294195912\", \"0.08457587414417689\", \"0.09803278069196336\"], \"16\": [\"0.8564236760139465\", \"0.9211616658239623\", \"0.8161455066742371\", \"0.7692510919701953\", \"0.9768927991487647\"], \"17\": [\"0.18138155341148376\", \"0.3836534063808792\", \"0.32758153272326956\", \"0.4113266996701133\", \"0.570285361295733\"], \"18\": [\"0.9790164232254028\", \"1.0\", \"1.0\", \"1.0\", \"0.964453883754515\"], \"19\": [\"0.3499999940395355\", \"0.3863954430145741\", \"0.24755652007061535\", \"0.5051159855045335\", \"0.49375728106831906\"], \"20\": [\"0.26923078298568726\", \"0.3038250697216256\", \"0.25850919457814997\", \"0.2518948811458642\", \"0.4459606481557118\"], \"21\": [\"0.30434781312942505\", \"0.23824684326423057\", \"0.20321435109957497\", \"0.2935219811348861\", \"0.31346873379591406\"], \"22\": [\"0.9008569717407227\", \"1.0\", \"1.0\", \"0.9140278427225832\", \"1.0\"], \"23\": [\"0.28123924136161804\", \"0.3762084986412605\", \"0.3306538281125844\", \"0.24729012034954972\", \"0.30802978646595547\"], \"24\": [\"0.9761654138565063\", \"1.0\", \"0.9389906906786338\", \"0.8338169486377209\", \"0.9372759027305281\"], \"25\": [\"0.31111112236976624\", \"0.3208377413980842\", \"0.3948946957047839\", \"0.31539736452749256\", \"0.3728005225544868\"], \"26\": [\"0.27450981736183167\", \"0.27961555028769025\", \"0.2999792434857541\", \"0.31526039999962985\", \"0.27444905495904626\"], \"27\": [\"0.2916666567325592\", \"0.25051975408216337\", \"0.31948167829640195\", \"0.29195289901624977\", \"0.19997201586106395\"], \"28\": [\"0.8802536129951477\", \"0.8857792893282364\", \"0.9536138581831235\", \"1.0\", \"0.9655741864371827\"], \"29\": [\"0.21615521609783173\", \"0.24735582070032103\", \"0.20091912397467232\", \"0.30567041309612497\", \"0.4337665892419137\"]}");
   var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\", \"25\": \"0.7\", \"26\": \"0.7\", \"27\": \"0.7\", \"28\": \"0.7\", \"29\": \"0.7\"}");
-  var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": false, \"3\": false, \"4\": true, \"5\": false, \"6\": false, \"7\": false, \"8\": false, \"9\": false, \"10\": true, \"11\": false, \"12\": true, \"13\": false, \"14\": false, \"15\": false, \"16\": true, \"17\": false, \"18\": false, \"19\": false, \"20\": false, \"21\": false, \"22\": true, \"23\": false, \"24\": true, \"25\": false, \"26\": false, \"27\": false, \"28\": true, \"29\": false}");
+  var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": false, \"3\": false, \"4\": true, \"5\": false, \"6\": true, \"7\": false, \"8\": false, \"9\": false, \"10\": true, \"11\": false, \"12\": true, \"13\": false, \"14\": false, \"15\": false, \"16\": true, \"17\": false, \"18\": true, \"19\": false, \"20\": false, \"21\": false, \"22\": true, \"23\": false, \"24\": true, \"25\": false, \"26\": false, \"27\": false, \"28\": true, \"29\": false}");
   var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"AveragePrecision\", \"6\": \"Accuracy\", \"7\": \"Precision\", \"8\": \"Recall\", \"9\": \"F1 Score\", \"10\": \"AUROC\", \"11\": \"AveragePrecision\", \"12\": \"Accuracy\", \"13\": \"Precision\", \"14\": \"Recall\", \"15\": \"F1 Score\", \"16\": \"AUROC\", \"17\": \"AveragePrecision\", \"18\": \"Accuracy\", \"19\": \"Precision\", \"20\": \"Recall\", \"21\": \"F1 Score\", \"22\": \"AUROC\", \"23\": \"AveragePrecision\", \"24\": \"Accuracy\", \"25\": \"Precision\", \"26\": \"Recall\", \"27\": \"F1 Score\", \"28\": \"AUROC\", \"29\": \"AveragePrecision\"}");
   var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"25\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"26\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"27\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"28\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"29\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
 
@@ -3484,7 +3484,7 @@ <h3>Ethical Considerations</h3>
   ];
 
   // create global variable for max_n_evals
-  var histories = JSON.parse("{\"0\": [\"0.9955621361732483\", \"1.0\", \"0.9090785186092738\", \"0.9335863596165629\", \"0.8186038363208302\"], \"1\": [\"1.0\", \"0.8456962509602705\", \"0.9355320907624267\", \"1.0\", \"0.9236438945572603\"], \"2\": [\"0.25\", \"0.30175867417868596\", \"0.4090271995112156\", \"0.642184940598215\", \"0.5625667826893166\"], \"3\": [\"0.4000000059604645\", \"0.469665571695541\", \"0.46555891294035706\", \"0.4246929861436543\", \"0.4764817145865098\"], \"4\": [\"0.9386160373687744\", \"0.9080135439423599\", \"0.718799557764541\", \"0.7718448217725096\", \"0.8908813868319999\"], \"5\": [\"0.3177526295185089\", \"0.4335309481655152\", \"0.4807214483612478\", \"0.4444917218015315\", \"0.5549339173062351\"], \"6\": [\"0.9685573577880859\", \"1.0\", \"0.8178219968390712\", \"0.7003549365904126\", \"0.6003240792292783\"], \"7\": [\"0.30000001192092896\", \"0.35909826175864534\", \"0.47348944179776753\", \"0.5569090961800103\", \"0.6604832940331692\"], \"8\": [\"0.34285715222358704\", \"0.37150638217185034\", \"0.2844859307436997\", \"0.27537346422318676\", \"0.37507420184202916\"], \"9\": [\"0.3199999928474426\", \"0.49736885117328794\", \"0.5623958932500298\", \"0.5664318809645187\", \"0.5967372035678011\"], \"10\": [\"0.8876226544380188\", \"0.8737110864533941\", \"1.0\", \"1.0\", \"1.0\"], \"11\": [\"0.2625003457069397\", \"0.2108439609683797\", \"0.0856302249586883\", \"0.0\", \"0.029420203307738438\"], \"12\": [\"0.9683908224105835\", \"0.9497491944262126\", \"0.9844978426087068\", \"1.0\", \"1.0\"], \"13\": [\"0.25\", \"0.3762954716415773\", \"0.45456463582296663\", \"0.5339118438393122\", \"0.5069639029619843\"], \"14\": [\"0.3461538553237915\", \"0.4947142590995716\", \"0.6704204325189426\", \"0.43423251643343996\", \"0.33013811548328653\"], \"15\": [\"0.29032257199287415\", \"0.35936163576848845\", \"0.2716241704399406\", \"0.2873628724031001\", \"0.38412138680037927\"], \"16\": [\"0.8653283715248108\", \"0.7291528249452761\", \"0.7700926939846393\", \"0.8493640536679986\", \"0.8657139349432235\"], \"17\": [\"0.27834367752075195\", \"0.15908432828490746\", \"0.09953745722591939\", \"0.05865593901864767\", \"0.06450930931244318\"], \"18\": [\"0.9780972003936768\", \"0.7810265759607673\", \"0.6391780088514672\", \"0.4022863555958669\", \"0.3127863936260465\"], \"19\": [\"0.36000001430511475\", \"0.3972208850236971\", \"0.3631355792628282\", \"0.27886805399935\", \"0.1472883753069758\"], \"20\": [\"0.36000001430511475\", \"0.3463096235651758\", \"0.3282687398531431\", \"0.1909615411077286\", \"0.3170350839086391\"], \"21\": [\"0.36000001430511475\", \"0.37975933594401634\", \"0.3595360205520707\", \"0.33289765378650693\", \"0.33517848204054623\"], \"22\": [\"0.9137047529220581\", \"0.7762175331349029\", \"0.9065865605338307\", \"0.8949404254956155\", \"1.0\"], \"23\": [\"0.2226799726486206\", \"0.23475933338139618\", \"0.30145735120000094\", \"0.4760373158972946\", \"0.5733008455410238\"], \"24\": [\"0.9733613729476929\", \"1.0\", \"1.0\", \"1.0\", \"0.9458611099827602\"], \"25\": [\"0.2950819730758667\", \"0.17918503949614295\", \"0.16078404851913272\", \"0.2286724166660674\", \"0.33070381701707174\"], \"26\": [\"0.3529411852359772\", \"0.3284656940326658\", \"0.09462185151495309\", \"0.01763941619196635\", \"0.17240288369907394\"], \"27\": [\"0.3214285671710968\", \"0.36647245688435953\", \"0.4894612008120508\", \"0.2919148118470184\", \"0.16720726225318921\"], \"28\": [\"0.8884620666503906\", \"0.9653942632935267\", \"0.9927010081623271\", \"0.9495828470650614\", \"0.8467377469882281\"], \"29\": [\"0.2392815202474594\", \"0.20562696816732118\", \"0.2705518602353964\", \"0.18884185073779158\", \"0.0557719953685491\"]}");
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index 56d8f6a69..21a813919 100644
--- a/api/tutorials/nihcxr/cxr_classification.html
+++ b/api/tutorials/nihcxr/cxr_classification.html
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diff --git a/api/tutorials/nihcxr/cxr_classification.ipynb b/api/tutorials/nihcxr/cxr_classification.ipynb
index ba41cc047..08fd3a94d 100644
--- a/api/tutorials/nihcxr/cxr_classification.ipynb
+++ b/api/tutorials/nihcxr/cxr_classification.ipynb
@@ -24,10 +24,10 @@
    "id": "fc1eb72a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T16:00:27.238351Z",
-     "iopub.status.busy": "2024-06-22T16:00:27.237401Z",
-     "iopub.status.idle": "2024-06-22T16:00:44.821949Z",
-     "shell.execute_reply": "2024-06-22T16:00:44.819783Z"
+     "iopub.execute_input": "2024-06-23T22:02:21.218105Z",
+     "iopub.status.busy": "2024-06-23T22:02:21.217108Z",
+     "iopub.status.idle": "2024-06-23T22:02:38.876547Z",
+     "shell.execute_reply": "2024-06-23T22:02:38.874123Z"
     }
    },
    "outputs": [],
@@ -69,10 +69,10 @@
    "id": "25c2a16f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T16:00:44.831651Z",
-     "iopub.status.busy": "2024-06-22T16:00:44.829694Z",
-     "iopub.status.idle": "2024-06-22T16:25:39.118818Z",
-     "shell.execute_reply": "2024-06-22T16:25:39.116191Z"
+     "iopub.execute_input": "2024-06-23T22:02:38.887710Z",
+     "iopub.status.busy": "2024-06-23T22:02:38.884978Z",
+     "iopub.status.idle": "2024-06-23T22:26:14.057193Z",
+     "shell.execute_reply": "2024-06-23T22:26:14.054621Z"
     },
     "nbsphinx": "hidden"
    },
@@ -90,7 +90,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Flattening the indices: 100%|########| 1000/1000 [04:18<00:00,  3.87 examples/s]"
+      "Flattening the indices: 100%|########| 1000/1000 [04:22<00:00,  3.81 examples/s]"
      ]
     },
     {
@@ -98,10 +98,16 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Flattening the indices: 100%|########| 1000/1000 [04:18<00:00,  3.87 examples/s]\r\n",
+      "Flattening the indices: 100%|########| 1000/1000 [04:22<00:00,  3.80 examples/s]\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\r",
       "Flattening the indices:   0%|                   | 0/1000 [00:00<?, ? examples/s]\r",
-      "Flattening the indices: 100%|####| 1000/1000 [00:00<00:00, 177709.69 examples/s]\r\n"
+      "Flattening the indices: 100%|#####| 1000/1000 [00:00<00:00, 74947.80 examples/s]\r\n"
      ]
     },
     {
@@ -109,8 +115,15 @@
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+      "Filter:   0%|                                   | 0/1000 [00:00<?, ? examples/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
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       "\r",
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      ]
@@ -120,7 +133,7 @@
      "output_type": "stream",
      "text": [
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+      "Map:  40%|##########8                | 161/400 [00:00<00:00, 1499.51 examples/s]"
      ]
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@@ -128,7 +141,7 @@
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      ]
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     {
@@ -136,8 +149,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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-      "Filter -> Patient Gender:M: 100%|###| 400/400 [00:00<00:00, 37688.06 examples/s]\r\n"
+      "Map: 100%|############################| 400/400 [00:00<00:00, 668.16 examples/s]\r\n"
      ]
     },
     {
@@ -145,7 +157,8 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Gender:F:   0%|                | 0/400 [00:00<?, ? examples/s]"
+      "Filter -> Patient Gender:M:   0%|                | 0/400 [00:00<?, ? examples/s]\r",
+      "Filter -> Patient Gender:M: 100%|###| 400/400 [00:00<00:00, 36546.10 examples/s]\r\n"
      ]
     },
     {
@@ -153,9 +166,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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-      "\r",
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+      "Filter -> Patient Gender:F:   0%|                | 0/400 [00:00<?, ? examples/s]"
      ]
     },
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@@ -163,7 +174,7 @@
      "output_type": "stream",
      "text": [
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+      "Filter -> Patient Gender:F: 100%|###| 400/400 [00:00<00:00, 37044.79 examples/s]\r\n"
      ]
     },
     {
@@ -171,7 +182,8 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:[19 - 35]:   0%|           | 0/400 [00:00<?, ? examples/s]"
+      "Filter -> overall:   0%|                         | 0/400 [00:00<?, ? examples/s]\r",
+      "Filter -> overall: 100%|############| 400/400 [00:00<00:00, 40691.77 examples/s]\r\n"
      ]
     },
     {
@@ -179,7 +191,8 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Filter -> Patient Age:[19 - 35]: 100%|#| 400/400 [00:00<00:00, 31785.87 examples\r\n",
       "\r",
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      ]
@@ -189,7 +202,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Filter -> Patient Age:[35 - 65]: 100%|#| 400/400 [00:00<00:00, 33797.78 examples\r\n"
      ]
     },
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@@ -198,9 +211,7 @@
      "text": [
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-      "\r",
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+      "Filter -> Patient Age:[65 - 100]: 100%|#| 400/400 [00:00<00:00, 33662.83 example\r\n"
      ]
     },
     {
@@ -208,16 +219,18 @@
      "output_type": "stream",
      "text": [
       "\r",
+      "Filter -> Patient Age:[19 - 35]&Patient Gender:M:   0%| | 0/400 [00:00<?, ? exam\r",
       "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|#| 400/400 [00:00<00:00, \r\n",
       "\r",
-      "Filter -> Patient Age:[19 - 35]&Patient Gender:F:   0%| | 0/400 [00:00<?, ? exam\r",
-      "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|#| 400/400 [00:00<00:00, \r\n"
+      "Filter -> Patient Age:[19 - 35]&Patient Gender:F:   0%| | 0/400 [00:00<?, ? exam"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\r",
+      "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|#| 400/400 [00:00<00:00, \r\n",
       "\r",
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       "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|#| 400/400 [00:00<00:00, \r\n"
@@ -247,10 +260,7 @@
      "text": [
       "\r",
       "Filter -> Patient Age:[65 - 100]&Patient Gender:F:   0%| | 0/400 [00:00<?, ? exa\r",
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-      "\r",
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+      "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|#| 400/400 [00:00<00:00,\r\n"
      ]
     },
     {
@@ -258,7 +268,8 @@
      "output_type": "stream",
      "text": [
       "\r",
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      ]
     },
     {
@@ -266,7 +277,7 @@
      "output_type": "stream",
      "text": [
       "\r",
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+      "Flattening the indices:   0%|                   | 0/1000 [00:00<?, ? examples/s]"
      ]
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@@ -274,13 +285,7 @@
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-      "\r",
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+      "Flattening the indices: 100%|########| 1000/1000 [03:47<00:00,  4.40 examples/s]"
      ]
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     {
@@ -288,7 +293,10 @@
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+      "Flattening the indices: 100%|########| 1000/1000 [03:47<00:00,  4.40 examples/s]\r\n",
+      "\r",
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      ]
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@@ -296,7 +304,10 @@
      "output_type": "stream",
      "text": [
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+      "\r",
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      ]
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@@ -304,7 +315,7 @@
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      ]
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@@ -312,7 +323,7 @@
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      ]
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     {
@@ -320,8 +331,9 @@
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+      "Map: 100%|############################| 396/396 [00:00<00:00, 724.35 examples/s]\r\n",
+      "\r",
+      "Filter -> Patient Gender:M:   0%|                | 0/396 [00:00<?, ? examples/s]"
      ]
     },
     {
@@ -329,8 +341,7 @@
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+      "Filter -> Patient Gender:M: 100%|###| 396/396 [00:00<00:00, 37665.70 examples/s]\r\n"
      ]
     },
     {
@@ -338,7 +349,8 @@
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      ]
     },
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@@ -346,7 +358,8 @@
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      ]
     },
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@@ -354,18 +367,17 @@
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      "text": [
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      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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+      "Filter -> Patient Age:[35 - 65]: 100%|#| 396/396 [00:00<00:00, 34426.57 examples\r\n"
      ]
     },
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@@ -374,9 +386,7 @@
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-      "\r",
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      ]
     },
     {
@@ -384,16 +394,18 @@
      "output_type": "stream",
      "text": [
       "\r",
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       "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|#| 396/396 [00:00<00:00, \r\n",
       "\r",
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+      "Filter -> Patient Age:[19 - 35]&Patient Gender:F:   0%| | 0/396 [00:00<?, ? exam"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\r",
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       "\r",
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@@ -414,9 +426,7 @@
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      ]
     },
     {
@@ -424,10 +434,18 @@
      "output_type": "stream",
      "text": [
       "\r",
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       "\r",
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+      "Filter -> overall:   0%|                         | 0/396 [00:00<?, ? examples/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
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      ]
     },
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@@ -443,8 +461,8 @@
      "output_type": "stream",
      "text": [
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      ]
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@@ -453,10 +471,10 @@
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       "\r",
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      ]
     },
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@@ -472,7 +490,7 @@
      "output_type": "stream",
      "text": [
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      ]
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     {
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      ]
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       "\r",
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      ]
@@ -498,7 +516,7 @@
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      "text": [
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      ]
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      ]
     },
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+      "\r",
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      ]
     },
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@@ -533,8 +553,7 @@
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      ]
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-     ]
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@@ -626,10 +638,12 @@
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-      "Flattening the indices: 100%|########| 1000/1000 [04:30<00:00,  3.69 examples/s]\r\n",
+      "Flattening the indices: 100%|########| 1000/1000 [03:47<00:00,  4.40 examples/s]\r\n",
       "\r",
       "Flattening the indices:   0%|                   | 0/1000 [00:00<?, ? examples/s]\r",
-      "Flattening the indices: 100%|####| 1000/1000 [00:00<00:00, 172968.12 examples/s]\r\n"
+      "Flattening the indices: 100%|####| 1000/1000 [00:00<00:00, 184787.38 examples/s]\r\n",
+      "\r",
+      "Filter:   0%|                                   | 0/1000 [00:00<?, ? examples/s]"
      ]
     },
     {
@@ -637,8 +651,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter:   0%|                                   | 0/1000 [00:00<?, ? examples/s]\r",
-      "Filter: 100%|#####################| 1000/1000 [00:00<00:00, 60024.96 examples/s]\r\n",
+      "Filter: 100%|#####################| 1000/1000 [00:00<00:00, 60427.08 examples/s]\r\n",
       "\r",
       "Map:   0%|                                       | 0/411 [00:00<?, ? examples/s]"
      ]
@@ -648,7 +661,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map:  39%|##########5                | 161/411 [00:00<00:00, 1486.51 examples/s]"
+      "Map:  39%|##########5                | 161/411 [00:00<00:00, 1492.88 examples/s]"
      ]
     },
     {
@@ -656,7 +669,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map:  79%|#####################2     | 323/411 [00:00<00:00, 1561.13 examples/s]"
+      "Map:  79%|#####################2     | 323/411 [00:00<00:00, 1563.61 examples/s]"
      ]
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     {
@@ -664,7 +677,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Map: 100%|############################| 411/411 [00:00<00:00, 657.43 examples/s]\r\n"
+      "Map: 100%|############################| 411/411 [00:00<00:00, 686.93 examples/s]\r\n"
      ]
     },
     {
@@ -673,7 +686,7 @@
      "text": [
       "\r",
       "Filter -> Patient Gender:M:   0%|                | 0/411 [00:00<?, ? examples/s]\r",
-      "Filter -> Patient Gender:M: 100%|###| 411/411 [00:00<00:00, 37304.08 examples/s]\r\n"
+      "Filter -> Patient Gender:M: 100%|###| 411/411 [00:00<00:00, 37922.85 examples/s]\r\n"
      ]
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     {
@@ -689,7 +702,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Gender:F: 100%|###| 411/411 [00:00<00:00, 37998.08 examples/s]\r\n"
+      "Filter -> Patient Gender:F: 100%|###| 411/411 [00:00<00:00, 38649.81 examples/s]\r\n"
      ]
     },
     {
@@ -698,7 +711,7 @@
      "text": [
       "\r",
       "Filter -> overall:   0%|                         | 0/411 [00:00<?, ? examples/s]\r",
-      "Filter -> overall: 100%|############| 411/411 [00:00<00:00, 42053.55 examples/s]\r\n"
+      "Filter -> overall: 100%|############| 411/411 [00:00<00:00, 42724.77 examples/s]\r\n"
      ]
     },
     {
@@ -707,7 +720,9 @@
      "text": [
       "\r",
       "Filter -> Patient Age:[19 - 35]:   0%|           | 0/411 [00:00<?, ? examples/s]\r",
-      "Filter -> Patient Age:[19 - 35]: 100%|#| 411/411 [00:00<00:00, 33298.41 examples\r\n"
+      "Filter -> Patient Age:[19 - 35]: 100%|#| 411/411 [00:00<00:00, 33878.88 examples\r\n",
+      "\r",
+      "Filter -> Patient Age:[35 - 65]:   0%|           | 0/411 [00:00<?, ? examples/s]"
      ]
     },
     {
@@ -715,8 +730,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:[35 - 65]:   0%|           | 0/411 [00:00<?, ? examples/s]\r",
-      "Filter -> Patient Age:[35 - 65]: 100%|#| 411/411 [00:00<00:00, 34794.50 examples\r\n"
+      "Filter -> Patient Age:[35 - 65]: 100%|#| 411/411 [00:00<00:00, 34522.06 examples\r\n"
      ]
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     {
@@ -725,7 +739,7 @@
      "text": [
       "\r",
       "Filter -> Patient Age:[65 - 100]:   0%|          | 0/411 [00:00<?, ? examples/s]\r",
-      "Filter -> Patient Age:[65 - 100]: 100%|#| 411/411 [00:00<00:00, 35477.65 example\r\n"
+      "Filter -> Patient Age:[65 - 100]: 100%|#| 411/411 [00:00<00:00, 34867.70 example\r\n"
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     {
@@ -774,7 +788,9 @@
      "text": [
       "\r",
       "Filter -> Patient Age:[65 - 100]&Patient Gender:F:   0%| | 0/411 [00:00<?, ? exa\r",
-      "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|#| 411/411 [00:00<00:00,\r\n"
+      "Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|#| 411/411 [00:00<00:00,\r\n",
+      "\r",
+      "Filter -> overall:   0%|                         | 0/411 [00:00<?, ? examples/s]"
      ]
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     {
@@ -782,8 +798,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> overall:   0%|                         | 0/411 [00:00<?, ? examples/s]\r",
-      "Filter -> overall: 100%|############| 411/411 [00:00<00:00, 43252.18 examples/s]\r\n"
+      "Filter -> overall: 100%|############| 411/411 [00:00<00:00, 43477.99 examples/s]\r\n"
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@@ -831,10 +846,10 @@
    "id": "03edf1c0",
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-     "shell.execute_reply": "2024-06-22T16:25:39.138559Z"
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@@ -856,10 +871,10 @@
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@@ -903,17 +918,17 @@
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-     "shell.execute_reply": "2024-06-22T16:26:30.304473Z"
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@@ -927,7 +942,7 @@
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@@ -987,17 +1002,17 @@
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-     "shell.execute_reply": "2024-06-22T16:26:30.725270Z"
+     "iopub.execute_input": "2024-06-23T22:27:10.921443Z",
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+     "shell.execute_reply": "2024-06-23T22:27:11.333673Z"
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@@ -1011,7 +1026,7 @@
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@@ -1025,7 +1040,7 @@
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@@ -1103,17 +1118,17 @@
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@@ -1302,10 +1317,10 @@
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-     "shell.execute_reply": "2024-06-22T16:26:32.557552Z"
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@@ -2200,9 +2215,9 @@
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@@ -4844,10 +4859,10 @@
    "id": "ec627997",
    "metadata": {
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-     "iopub.status.idle": "2024-06-22T16:26:37.598696Z",
-     "shell.execute_reply": "2024-06-22T16:26:37.596954Z"
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+     "iopub.status.idle": "2024-06-23T22:27:18.258289Z",
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@@ -4905,10 +4920,10 @@
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index 1307b6a4c..560d9b9c0 100644
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@@ -3827,7 +3827,7 @@ <h3>Graphics</h3>
     
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@@ -3835,7 +3835,7 @@ <h3>Graphics</h3>
     
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+    <div>                            <div id="23e09f40-b19f-4522-9629-cc83304ec7d5" class="plotly-graph-div" style="height:100%; width:100%;"></div>            <script type="text/javascript">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("23e09f40-b19f-4522-9629-cc83304ec7d5")) {                    Plotly.newPlot(                        "23e09f40-b19f-4522-9629-cc83304ec7d5",                        [{"x":[2,4,3,5,8,9,10,7,13,6,11,12,17,14,15,16,20,19,18],"y":[232,184,152,117,76,65,47,39,38,38,34,27,16,15,12,12,12,5,5],"type":"bar"}],                        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 </div>
 
@@ -1032,7 +1032,7 @@ <h3>Graphics</h3>
     
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-        <span style="font-size: 48px; font-weight:bolder; color: green">0.7</span>
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 </div>
 
@@ -1428,7 +1428,7 @@ <h3>Version</h3>
         
           
             
-              <b>Date:</b> 2024-06-22
+              <b>Date:</b> 2024-06-23
             
           
           <br>
@@ -1646,24 +1646,20 @@ <h2 class="collapsible">Model Parameters</h2>
 
   
     
-    <div class="subcard">
-      <h3>Learning_rate</h3>
-      0.01
-    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Min_child_weight</h3>
-      3
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Enable_categorical</h3>
+      False
+    </div>
     
   
 
@@ -1674,10 +1670,6 @@ <h3>Min_child_weight</h3>
 
   
     
-    <div class="subcard">
-      <h3>Missing</h3>
-      nan
-    </div>
     
   
 
@@ -1693,6 +1685,10 @@ <h3>Missing</h3>
 
   
     
+    <div class="subcard">
+      <h3>Reg_lambda</h3>
+      0
+    </div>
     
   
 
@@ -1703,6 +1699,10 @@ <h3>Missing</h3>
 
   
     
+    <div class="subcard">
+      <h3>Gamma</h3>
+      10
+    </div>
     
   
 
@@ -1714,8 +1714,8 @@ <h3>Missing</h3>
   
     
     <div class="subcard">
-      <h3>Colsample_bytree</h3>
-      1
+      <h3>Min_child_weight</h3>
+      3
     </div>
     
   
@@ -1742,11 +1742,19 @@ <h3>Colsample_bytree</h3>
 
   
     
+    <div class="subcard">
+      <h3>Colsample_bytree</h3>
+      0.8
+    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Max_depth</h3>
+      5
+    </div>
     
   
 
@@ -1757,15 +1765,15 @@ <h3>Colsample_bytree</h3>
 
   
     
+    <div class="subcard">
+      <h3>Learning_rate</h3>
+      0.1
+    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Seed</h3>
-      123
-    </div>
     
   
 
@@ -1776,10 +1784,6 @@ <h3>Seed</h3>
 
   
     
-    <div class="subcard">
-      <h3>Random_state</h3>
-      123
-    </div>
     
   
 
@@ -1790,6 +1794,10 @@ <h3>Random_state</h3>
 
   
     
+    <div class="subcard">
+      <h3>Missing</h3>
+      nan
+    </div>
     
   
 
@@ -1800,15 +1808,15 @@ <h3>Random_state</h3>
 
   
     
-    <div class="subcard">
-      <h3>Eval_metric</h3>
-      logloss
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Random_state</h3>
+      123
+    </div>
     
   
 
@@ -1825,8 +1833,8 @@ <h3>Eval_metric</h3>
   
     
     <div class="subcard">
-      <h3>Reg_lambda</h3>
-      1
+      <h3>Eval_metric</h3>
+      logloss
     </div>
     
   
@@ -1834,45 +1842,33 @@ <h3>Reg_lambda</h3>
   
     
     <div class="subcard">
-      <h3>Gamma</h3>
-      2
+      <h3>Objective</h3>
+      binary:logistic
     </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Objective</h3>
-      binary:logistic
-    </div>
     
   
 
   
     
     <div class="subcard">
-      <h3>Max_depth</h3>
-      5
+      <h3>Seed</h3>
+      123
     </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Enable_categorical</h3>
-      False
-    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>N_estimators</h3>
-      100
-    </div>
     
   
 
@@ -1883,6 +1879,10 @@ <h3>N_estimators</h3>
 
   
     
+    <div class="subcard">
+      <h3>N_estimators</h3>
+      500
+    </div>
     
   
 
@@ -2267,9 +2267,9 @@ <h3>Ethical Considerations</h3>
       }
   }
   var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}");
-  var histories_all = JSON.parse("{\"0\": [\"0.84112149477005\", \"0.9817659303447434\", \"0.8950642140691593\", \"0.9047708858422704\", \"0.9240841660279289\"], \"1\": [\"0.9555555582046509\", \"0.7803277976883027\", \"0.8510041982062708\", \"0.9000723593017211\", \"1.0\"], \"2\": [\"0.7413793206214905\", \"0.6588479609880967\", \"0.6311668473050613\", \"0.6341035914470685\", \"0.6020137616707144\"], \"3\": [\"0.8349514603614807\", \"0.8228705446883667\", \"0.8012933855069491\", \"0.8956310859468717\", \"0.9361457686799274\"], \"4\": [\"0.9146727919578552\", \"1.0\", \"1.0\", \"0.8994298614947843\", \"0.8441834511034\"], \"5\": [\"0.8888888955116272\", \"0.8105073804259116\", \"0.9773840905602402\", \"1.0\", \"1.0\"], \"6\": [\"0.9354838728904724\", \"1.0\", \"0.8172363024977958\", \"0.7303185465744566\", \"0.5533207248029539\"], \"7\": [\"0.8787878751754761\", \"0.821455378488748\", \"0.7852847280257992\", \"0.9613709424345702\", \"0.9820705430287403\"], \"8\": [\"0.90625\", \"0.8539429816108812\", \"0.7742761725291468\", \"0.6906901181257419\", \"0.5999571024637336\"], \"9\": [\"0.9567099809646606\", \"0.9425927224290881\", \"0.8700307419709208\", \"0.8030962997928685\", \"0.8649263011547226\"], \"10\": [\"0.8595041036605835\", \"0.7275597396343939\", \"0.7629427420711723\", \"0.8236803124777468\", \"0.6506962436114708\"], \"11\": [\"0.9558823704719543\", \"1.0\", \"0.9221465628594138\", \"1.0\", \"1.0\"], \"12\": [\"0.8227847814559937\", \"0.9341788610689488\", \"0.8618731577217749\", \"0.7347607774140208\", \"0.683279423912476\"], \"13\": [\"0.884353756904602\", \"1.0\", \"0.9494260008024844\", \"1.0\", \"0.9898660938325575\"], \"14\": [\"0.9324895143508911\", \"0.8609609563845063\", \"0.7207129374666734\", \"0.5898612807913485\", \"0.5384402853377004\"], \"15\": [\"0.8857142925262451\", \"0.7349006063721182\", \"0.6458812985164686\", \"0.6791880905418182\", \"0.7876028555392535\"], \"16\": [\"0.9354838728904724\", \"0.7812222315260559\", \"0.90453748894957\", \"1.0\", \"1.0\"], \"17\": [\"0.8787878751754761\", \"0.9818774452647927\", \"0.9754831851056254\", \"0.9758186874660493\", \"0.9972650285407768\"], \"18\": [\"0.90625\", \"0.9550546650748263\", \"0.9606845503722818\", \"0.9137895285173488\", \"0.9738208933981543\"], \"19\": [\"0.9316239356994629\", \"0.9621875079156207\", \"0.8917392818528632\", \"0.7668191207702044\", \"0.7224465529863837\"], \"20\": [\"0.8716813921928406\", \"0.8975055201908481\", \"0.9371161142125217\", \"0.896289550180259\", \"0.8989754400984284\"], \"21\": [\"0.9461538195610046\", \"1.0\", \"0.9581686944948844\", \"0.9334275450453174\", \"0.804875830655667\"], \"22\": [\"0.8482758402824402\", \"0.7849792752853116\", \"0.7871072402207281\", \"0.850798012791235\", \"0.7500798466560521\"], \"23\": [\"0.8945454359054565\", \"0.8220115597957549\", \"0.8854937464247236\", \"0.9468292996157057\", \"0.882895949481078\"], \"24\": [\"0.932354211807251\", \"0.775022271848009\", \"0.7481924615850153\", \"0.7387783704247286\", \"0.7009866864848837\"]}");
+  var histories_all = JSON.parse("{\"0\": [\"0.8636363744735718\", \"0.92886081112948\", \"1.0\", \"0.8388038334314838\", \"0.8271875657967567\"], \"1\": [\"0.9795918464660645\", \"1.0\", \"0.982543943359856\", \"1.0\", \"1.0\"], \"2\": [\"0.774193525314331\", \"0.8243560838953359\", \"0.995089381881134\", \"1.0\", \"0.9412026289459778\"], \"3\": [\"0.8648648858070374\", \"0.8162655767088807\", \"0.7795336085778017\", \"0.7411887914229585\", \"0.8083397554971863\"], \"4\": [\"0.9410282373428345\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"5\": [\"0.8947368264198303\", \"1.0\", \"1.0\", \"0.9417175273644387\", \"1.0\"], \"6\": [\"0.875\", \"0.8395464300133455\", \"0.7456776102241438\", \"0.7379193629190488\", \"0.7301023910352686\"], \"7\": [\"0.9333333373069763\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"8\": [\"0.9032257795333862\", \"0.8832691274412479\", \"0.898138741685616\", \"0.8053984822297517\", \"0.7001154768020333\"], \"9\": [\"0.9358024001121521\", \"0.723815473128497\", \"0.7624543787388742\", \"0.822502223319343\", \"0.8275619527545803\"], \"10\": [\"0.8861788511276245\", \"0.9405222040176702\", \"0.9577679141433092\", \"0.8603178410811596\", \"0.9197755518663465\"], \"11\": [\"0.9750000238418579\", \"0.8782885107832065\", \"0.8367072960272697\", \"0.7986760129835162\", \"0.9105716497643859\"], \"12\": [\"0.8666666746139526\", \"0.9251160091099873\", \"0.746263327514448\", \"0.8748620924287724\", \"1.0\"], \"13\": [\"0.9176470637321472\", \"0.8991453249174426\", \"1.0\", \"0.9811549933268298\", \"0.9570742194944619\"], \"14\": [\"0.9537037014961243\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.9126213788986206\", \"0.95590361651259\", \"0.8577341451663933\", \"0.799358726729822\", \"0.809309780504532\"], \"16\": [\"0.942307710647583\", \"0.9506323824318652\", \"1.0\", \"1.0\", \"1.0\"], \"17\": [\"0.8909090757369995\", \"0.9458597618150345\", \"1.0\", \"1.0\", \"0.9593212131992811\"], \"18\": [\"0.9158878326416016\", \"0.8326578128511968\", \"0.8022102546108545\", \"1.0\", \"0.8879098256031821\"], \"19\": [\"0.9621212482452393\", \"1.0\", \"0.9688050743369877\", \"0.9954941235609506\", \"0.9783148891769534\"], \"20\": [\"0.8982300758361816\", \"0.857543273758781\", \"0.904582479269161\", \"0.9808626574410144\", \"0.8590755384678712\"], \"21\": [\"0.9621211886405945\", \"1.0\", \"1.0\", \"1.0\", \"0.9915753120646987\"], \"22\": [\"0.8758620619773865\", \"1.0\", \"1.0\", \"1.0\", \"0.9595588659073383\"], \"23\": [\"0.916967511177063\", \"0.9251057231160493\", \"0.9417636941765279\", \"1.0\", \"0.8515125521376568\"], \"24\": [\"0.9577693343162537\", \"0.9319297591011309\", \"0.9925472053943316\", \"1.0\", \"0.7621692516448988\"]}");
   var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}");
-  var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": false, \"3\": true, \"4\": true, \"5\": true, \"6\": false, \"7\": true, \"8\": false, \"9\": true, \"10\": false, \"11\": true, \"12\": false, \"13\": true, \"14\": false, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}");
+  var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": true, \"4\": true, \"5\": true, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}");
   var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}");
   var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
 
@@ -2571,7 +2571,7 @@ <h3>Ethical Considerations</h3>
 function generate_model_card_plot() {
   var model_card_plots = []
   var overall_indices = [20, 21, 22, 23, 24]
-  var histories = JSON.parse("{\"0\": [\"0.84112149477005\", \"0.9817659303447434\", \"0.8950642140691593\", \"0.9047708858422704\", \"0.9240841660279289\"], \"1\": [\"0.9555555582046509\", \"0.7803277976883027\", \"0.8510041982062708\", \"0.9000723593017211\", \"1.0\"], \"2\": [\"0.7413793206214905\", \"0.6588479609880967\", \"0.6311668473050613\", \"0.6341035914470685\", \"0.6020137616707144\"], \"3\": [\"0.8349514603614807\", \"0.8228705446883667\", \"0.8012933855069491\", \"0.8956310859468717\", \"0.9361457686799274\"], \"4\": [\"0.9146727919578552\", \"1.0\", \"1.0\", \"0.8994298614947843\", \"0.8441834511034\"], \"5\": [\"0.8888888955116272\", \"0.8105073804259116\", \"0.9773840905602402\", \"1.0\", \"1.0\"], \"6\": [\"0.9354838728904724\", \"1.0\", \"0.8172363024977958\", \"0.7303185465744566\", \"0.5533207248029539\"], \"7\": [\"0.8787878751754761\", \"0.821455378488748\", \"0.7852847280257992\", \"0.9613709424345702\", \"0.9820705430287403\"], \"8\": [\"0.90625\", \"0.8539429816108812\", \"0.7742761725291468\", \"0.6906901181257419\", \"0.5999571024637336\"], \"9\": [\"0.9567099809646606\", \"0.9425927224290881\", \"0.8700307419709208\", \"0.8030962997928685\", \"0.8649263011547226\"], \"10\": [\"0.8595041036605835\", \"0.7275597396343939\", \"0.7629427420711723\", \"0.8236803124777468\", \"0.6506962436114708\"], \"11\": [\"0.9558823704719543\", \"1.0\", \"0.9221465628594138\", \"1.0\", \"1.0\"], \"12\": [\"0.8227847814559937\", \"0.9341788610689488\", \"0.8618731577217749\", \"0.7347607774140208\", \"0.683279423912476\"], \"13\": [\"0.884353756904602\", \"1.0\", \"0.9494260008024844\", \"1.0\", \"0.9898660938325575\"], \"14\": [\"0.9324895143508911\", \"0.8609609563845063\", \"0.7207129374666734\", \"0.5898612807913485\", \"0.5384402853377004\"], \"15\": [\"0.8857142925262451\", \"0.7349006063721182\", \"0.6458812985164686\", \"0.6791880905418182\", \"0.7876028555392535\"], \"16\": [\"0.9354838728904724\", \"0.7812222315260559\", \"0.90453748894957\", \"1.0\", \"1.0\"], \"17\": [\"0.8787878751754761\", \"0.9818774452647927\", \"0.9754831851056254\", \"0.9758186874660493\", \"0.9972650285407768\"], \"18\": [\"0.90625\", \"0.9550546650748263\", \"0.9606845503722818\", \"0.9137895285173488\", \"0.9738208933981543\"], \"19\": [\"0.9316239356994629\", \"0.9621875079156207\", \"0.8917392818528632\", \"0.7668191207702044\", \"0.7224465529863837\"], \"20\": [\"0.8716813921928406\", \"0.8975055201908481\", \"0.9371161142125217\", \"0.896289550180259\", \"0.8989754400984284\"], \"21\": [\"0.9461538195610046\", \"1.0\", \"0.9581686944948844\", \"0.9334275450453174\", \"0.804875830655667\"], \"22\": [\"0.8482758402824402\", \"0.7849792752853116\", \"0.7871072402207281\", \"0.850798012791235\", \"0.7500798466560521\"], \"23\": [\"0.8945454359054565\", \"0.8220115597957549\", \"0.8854937464247236\", \"0.9468292996157057\", \"0.882895949481078\"], \"24\": [\"0.932354211807251\", \"0.775022271848009\", \"0.7481924615850153\", \"0.7387783704247286\", \"0.7009866864848837\"]}");
+  var histories = JSON.parse("{\"0\": [\"0.8636363744735718\", \"0.92886081112948\", \"1.0\", \"0.8388038334314838\", \"0.8271875657967567\"], \"1\": [\"0.9795918464660645\", \"1.0\", \"0.982543943359856\", \"1.0\", \"1.0\"], \"2\": [\"0.774193525314331\", \"0.8243560838953359\", \"0.995089381881134\", \"1.0\", \"0.9412026289459778\"], \"3\": [\"0.8648648858070374\", \"0.8162655767088807\", \"0.7795336085778017\", \"0.7411887914229585\", \"0.8083397554971863\"], \"4\": [\"0.9410282373428345\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"5\": [\"0.8947368264198303\", \"1.0\", \"1.0\", \"0.9417175273644387\", \"1.0\"], \"6\": [\"0.875\", \"0.8395464300133455\", \"0.7456776102241438\", \"0.7379193629190488\", \"0.7301023910352686\"], \"7\": [\"0.9333333373069763\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"8\": [\"0.9032257795333862\", \"0.8832691274412479\", \"0.898138741685616\", \"0.8053984822297517\", \"0.7001154768020333\"], \"9\": [\"0.9358024001121521\", \"0.723815473128497\", \"0.7624543787388742\", \"0.822502223319343\", \"0.8275619527545803\"], \"10\": [\"0.8861788511276245\", \"0.9405222040176702\", \"0.9577679141433092\", \"0.8603178410811596\", \"0.9197755518663465\"], \"11\": [\"0.9750000238418579\", \"0.8782885107832065\", \"0.8367072960272697\", \"0.7986760129835162\", \"0.9105716497643859\"], \"12\": [\"0.8666666746139526\", \"0.9251160091099873\", \"0.746263327514448\", \"0.8748620924287724\", \"1.0\"], \"13\": [\"0.9176470637321472\", \"0.8991453249174426\", \"1.0\", \"0.9811549933268298\", \"0.9570742194944619\"], \"14\": [\"0.9537037014961243\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.9126213788986206\", \"0.95590361651259\", \"0.8577341451663933\", \"0.799358726729822\", \"0.809309780504532\"], \"16\": [\"0.942307710647583\", \"0.9506323824318652\", \"1.0\", \"1.0\", \"1.0\"], \"17\": [\"0.8909090757369995\", \"0.9458597618150345\", \"1.0\", \"1.0\", \"0.9593212131992811\"], \"18\": [\"0.9158878326416016\", \"0.8326578128511968\", \"0.8022102546108545\", \"1.0\", \"0.8879098256031821\"], \"19\": [\"0.9621212482452393\", \"1.0\", \"0.9688050743369877\", \"0.9954941235609506\", \"0.9783148891769534\"], \"20\": [\"0.8982300758361816\", \"0.857543273758781\", \"0.904582479269161\", \"0.9808626574410144\", \"0.8590755384678712\"], \"21\": [\"0.9621211886405945\", \"1.0\", \"1.0\", \"1.0\", \"0.9915753120646987\"], \"22\": [\"0.8758620619773865\", \"1.0\", \"1.0\", \"1.0\", \"0.9595588659073383\"], \"23\": [\"0.916967511177063\", \"0.9251057231160493\", \"0.9417636941765279\", \"1.0\", \"0.8515125521376568\"], \"24\": [\"0.9577693343162537\", \"0.9319297591011309\", \"0.9925472053943316\", \"1.0\", \"0.7621692516448988\"]}");
   var thresholds = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}");
   var timestamps = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
 
@@ -2871,9 +2871,9 @@ <h3>Ethical Considerations</h3>
       }
   }
   var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}");
-  var histories_all = JSON.parse("{\"0\": [\"0.84112149477005\", \"0.9817659303447434\", \"0.8950642140691593\", \"0.9047708858422704\", \"0.9240841660279289\"], \"1\": [\"0.9555555582046509\", \"0.7803277976883027\", \"0.8510041982062708\", \"0.9000723593017211\", \"1.0\"], \"2\": [\"0.7413793206214905\", \"0.6588479609880967\", \"0.6311668473050613\", \"0.6341035914470685\", \"0.6020137616707144\"], \"3\": [\"0.8349514603614807\", \"0.8228705446883667\", \"0.8012933855069491\", \"0.8956310859468717\", \"0.9361457686799274\"], \"4\": [\"0.9146727919578552\", \"1.0\", \"1.0\", \"0.8994298614947843\", \"0.8441834511034\"], \"5\": [\"0.8888888955116272\", \"0.8105073804259116\", \"0.9773840905602402\", \"1.0\", \"1.0\"], \"6\": [\"0.9354838728904724\", \"1.0\", \"0.8172363024977958\", \"0.7303185465744566\", \"0.5533207248029539\"], \"7\": [\"0.8787878751754761\", \"0.821455378488748\", \"0.7852847280257992\", \"0.9613709424345702\", \"0.9820705430287403\"], \"8\": [\"0.90625\", \"0.8539429816108812\", \"0.7742761725291468\", \"0.6906901181257419\", \"0.5999571024637336\"], \"9\": [\"0.9567099809646606\", \"0.9425927224290881\", \"0.8700307419709208\", \"0.8030962997928685\", \"0.8649263011547226\"], \"10\": [\"0.8595041036605835\", \"0.7275597396343939\", \"0.7629427420711723\", \"0.8236803124777468\", \"0.6506962436114708\"], \"11\": [\"0.9558823704719543\", \"1.0\", \"0.9221465628594138\", \"1.0\", \"1.0\"], \"12\": [\"0.8227847814559937\", \"0.9341788610689488\", \"0.8618731577217749\", \"0.7347607774140208\", \"0.683279423912476\"], \"13\": [\"0.884353756904602\", \"1.0\", \"0.9494260008024844\", \"1.0\", \"0.9898660938325575\"], \"14\": [\"0.9324895143508911\", \"0.8609609563845063\", \"0.7207129374666734\", \"0.5898612807913485\", \"0.5384402853377004\"], \"15\": [\"0.8857142925262451\", \"0.7349006063721182\", \"0.6458812985164686\", \"0.6791880905418182\", \"0.7876028555392535\"], \"16\": [\"0.9354838728904724\", \"0.7812222315260559\", \"0.90453748894957\", \"1.0\", \"1.0\"], \"17\": [\"0.8787878751754761\", \"0.9818774452647927\", \"0.9754831851056254\", \"0.9758186874660493\", \"0.9972650285407768\"], \"18\": [\"0.90625\", \"0.9550546650748263\", \"0.9606845503722818\", \"0.9137895285173488\", \"0.9738208933981543\"], \"19\": [\"0.9316239356994629\", \"0.9621875079156207\", \"0.8917392818528632\", \"0.7668191207702044\", \"0.7224465529863837\"], \"20\": [\"0.8716813921928406\", \"0.8975055201908481\", \"0.9371161142125217\", \"0.896289550180259\", \"0.8989754400984284\"], \"21\": [\"0.9461538195610046\", \"1.0\", \"0.9581686944948844\", \"0.9334275450453174\", \"0.804875830655667\"], \"22\": [\"0.8482758402824402\", \"0.7849792752853116\", \"0.7871072402207281\", \"0.850798012791235\", \"0.7500798466560521\"], \"23\": [\"0.8945454359054565\", \"0.8220115597957549\", \"0.8854937464247236\", \"0.9468292996157057\", \"0.882895949481078\"], \"24\": [\"0.932354211807251\", \"0.775022271848009\", \"0.7481924615850153\", \"0.7387783704247286\", \"0.7009866864848837\"]}");
+  var histories_all = JSON.parse("{\"0\": [\"0.8636363744735718\", \"0.92886081112948\", \"1.0\", \"0.8388038334314838\", \"0.8271875657967567\"], \"1\": [\"0.9795918464660645\", \"1.0\", \"0.982543943359856\", \"1.0\", \"1.0\"], \"2\": [\"0.774193525314331\", \"0.8243560838953359\", \"0.995089381881134\", \"1.0\", \"0.9412026289459778\"], \"3\": [\"0.8648648858070374\", \"0.8162655767088807\", \"0.7795336085778017\", \"0.7411887914229585\", \"0.8083397554971863\"], \"4\": [\"0.9410282373428345\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"5\": [\"0.8947368264198303\", \"1.0\", \"1.0\", \"0.9417175273644387\", \"1.0\"], \"6\": [\"0.875\", \"0.8395464300133455\", \"0.7456776102241438\", \"0.7379193629190488\", \"0.7301023910352686\"], \"7\": [\"0.9333333373069763\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"8\": [\"0.9032257795333862\", \"0.8832691274412479\", \"0.898138741685616\", \"0.8053984822297517\", \"0.7001154768020333\"], \"9\": [\"0.9358024001121521\", \"0.723815473128497\", \"0.7624543787388742\", \"0.822502223319343\", \"0.8275619527545803\"], \"10\": [\"0.8861788511276245\", \"0.9405222040176702\", \"0.9577679141433092\", \"0.8603178410811596\", \"0.9197755518663465\"], \"11\": [\"0.9750000238418579\", \"0.8782885107832065\", \"0.8367072960272697\", \"0.7986760129835162\", \"0.9105716497643859\"], \"12\": [\"0.8666666746139526\", \"0.9251160091099873\", \"0.746263327514448\", \"0.8748620924287724\", \"1.0\"], \"13\": [\"0.9176470637321472\", \"0.8991453249174426\", \"1.0\", \"0.9811549933268298\", \"0.9570742194944619\"], \"14\": [\"0.9537037014961243\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.9126213788986206\", \"0.95590361651259\", \"0.8577341451663933\", \"0.799358726729822\", \"0.809309780504532\"], \"16\": [\"0.942307710647583\", \"0.9506323824318652\", \"1.0\", \"1.0\", \"1.0\"], \"17\": [\"0.8909090757369995\", \"0.9458597618150345\", \"1.0\", \"1.0\", \"0.9593212131992811\"], \"18\": [\"0.9158878326416016\", \"0.8326578128511968\", \"0.8022102546108545\", \"1.0\", \"0.8879098256031821\"], \"19\": [\"0.9621212482452393\", \"1.0\", \"0.9688050743369877\", \"0.9954941235609506\", \"0.9783148891769534\"], \"20\": [\"0.8982300758361816\", \"0.857543273758781\", \"0.904582479269161\", \"0.9808626574410144\", \"0.8590755384678712\"], \"21\": [\"0.9621211886405945\", \"1.0\", \"1.0\", \"1.0\", \"0.9915753120646987\"], \"22\": [\"0.8758620619773865\", \"1.0\", \"1.0\", \"1.0\", \"0.9595588659073383\"], \"23\": [\"0.916967511177063\", \"0.9251057231160493\", \"0.9417636941765279\", \"1.0\", \"0.8515125521376568\"], \"24\": [\"0.9577693343162537\", \"0.9319297591011309\", \"0.9925472053943316\", \"1.0\", \"0.7621692516448988\"]}");
   var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}");
-  var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": false, \"3\": true, \"4\": true, \"5\": true, \"6\": false, \"7\": true, \"8\": false, \"9\": true, \"10\": false, \"11\": true, \"12\": false, \"13\": true, \"14\": false, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}");
+  var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": true, \"4\": true, \"5\": true, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}");
   var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}");
   var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"16\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"17\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"18\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"19\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"20\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"21\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"22\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"23\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"24\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
 
@@ -3386,7 +3386,7 @@ <h3>Ethical Considerations</h3>
   ];
 
   // create global variable for max_n_evals
-  var histories = JSON.parse("{\"0\": [\"0.84112149477005\", \"0.9817659303447434\", \"0.8950642140691593\", \"0.9047708858422704\", \"0.9240841660279289\"], \"1\": [\"0.9555555582046509\", \"0.7803277976883027\", \"0.8510041982062708\", \"0.9000723593017211\", \"1.0\"], \"2\": [\"0.7413793206214905\", \"0.6588479609880967\", \"0.6311668473050613\", \"0.6341035914470685\", \"0.6020137616707144\"], \"3\": [\"0.8349514603614807\", \"0.8228705446883667\", \"0.8012933855069491\", \"0.8956310859468717\", \"0.9361457686799274\"], \"4\": [\"0.9146727919578552\", \"1.0\", \"1.0\", \"0.8994298614947843\", \"0.8441834511034\"], \"5\": [\"0.8888888955116272\", \"0.8105073804259116\", \"0.9773840905602402\", \"1.0\", \"1.0\"], \"6\": [\"0.9354838728904724\", \"1.0\", \"0.8172363024977958\", \"0.7303185465744566\", \"0.5533207248029539\"], \"7\": [\"0.8787878751754761\", \"0.821455378488748\", \"0.7852847280257992\", \"0.9613709424345702\", \"0.9820705430287403\"], \"8\": [\"0.90625\", \"0.8539429816108812\", \"0.7742761725291468\", \"0.6906901181257419\", \"0.5999571024637336\"], \"9\": [\"0.9567099809646606\", \"0.9425927224290881\", \"0.8700307419709208\", \"0.8030962997928685\", \"0.8649263011547226\"], \"10\": [\"0.8595041036605835\", \"0.7275597396343939\", \"0.7629427420711723\", \"0.8236803124777468\", \"0.6506962436114708\"], \"11\": [\"0.9558823704719543\", \"1.0\", \"0.9221465628594138\", \"1.0\", \"1.0\"], \"12\": [\"0.8227847814559937\", \"0.9341788610689488\", \"0.8618731577217749\", \"0.7347607774140208\", \"0.683279423912476\"], \"13\": [\"0.884353756904602\", \"1.0\", \"0.9494260008024844\", \"1.0\", \"0.9898660938325575\"], \"14\": [\"0.9324895143508911\", \"0.8609609563845063\", \"0.7207129374666734\", \"0.5898612807913485\", \"0.5384402853377004\"], \"15\": [\"0.8857142925262451\", \"0.7349006063721182\", \"0.6458812985164686\", \"0.6791880905418182\", \"0.7876028555392535\"], \"16\": [\"0.9354838728904724\", \"0.7812222315260559\", \"0.90453748894957\", \"1.0\", \"1.0\"], \"17\": [\"0.8787878751754761\", \"0.9818774452647927\", \"0.9754831851056254\", \"0.9758186874660493\", \"0.9972650285407768\"], \"18\": [\"0.90625\", \"0.9550546650748263\", \"0.9606845503722818\", \"0.9137895285173488\", \"0.9738208933981543\"], \"19\": [\"0.9316239356994629\", \"0.9621875079156207\", \"0.8917392818528632\", \"0.7668191207702044\", \"0.7224465529863837\"], \"20\": [\"0.8716813921928406\", \"0.8975055201908481\", \"0.9371161142125217\", \"0.896289550180259\", \"0.8989754400984284\"], \"21\": [\"0.9461538195610046\", \"1.0\", \"0.9581686944948844\", \"0.9334275450453174\", \"0.804875830655667\"], \"22\": [\"0.8482758402824402\", \"0.7849792752853116\", \"0.7871072402207281\", \"0.850798012791235\", \"0.7500798466560521\"], \"23\": [\"0.8945454359054565\", \"0.8220115597957549\", \"0.8854937464247236\", \"0.9468292996157057\", \"0.882895949481078\"], \"24\": [\"0.932354211807251\", \"0.775022271848009\", \"0.7481924615850153\", \"0.7387783704247286\", \"0.7009866864848837\"]}");
+  var histories = JSON.parse("{\"0\": [\"0.8636363744735718\", \"0.92886081112948\", \"1.0\", \"0.8388038334314838\", \"0.8271875657967567\"], \"1\": [\"0.9795918464660645\", \"1.0\", \"0.982543943359856\", \"1.0\", \"1.0\"], \"2\": [\"0.774193525314331\", \"0.8243560838953359\", \"0.995089381881134\", \"1.0\", \"0.9412026289459778\"], \"3\": [\"0.8648648858070374\", \"0.8162655767088807\", \"0.7795336085778017\", \"0.7411887914229585\", \"0.8083397554971863\"], \"4\": [\"0.9410282373428345\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"5\": [\"0.8947368264198303\", \"1.0\", \"1.0\", \"0.9417175273644387\", \"1.0\"], \"6\": [\"0.875\", \"0.8395464300133455\", \"0.7456776102241438\", \"0.7379193629190488\", \"0.7301023910352686\"], \"7\": [\"0.9333333373069763\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"8\": [\"0.9032257795333862\", \"0.8832691274412479\", \"0.898138741685616\", \"0.8053984822297517\", \"0.7001154768020333\"], \"9\": [\"0.9358024001121521\", \"0.723815473128497\", \"0.7624543787388742\", \"0.822502223319343\", \"0.8275619527545803\"], \"10\": [\"0.8861788511276245\", \"0.9405222040176702\", \"0.9577679141433092\", \"0.8603178410811596\", \"0.9197755518663465\"], \"11\": [\"0.9750000238418579\", \"0.8782885107832065\", \"0.8367072960272697\", \"0.7986760129835162\", \"0.9105716497643859\"], \"12\": [\"0.8666666746139526\", \"0.9251160091099873\", \"0.746263327514448\", \"0.8748620924287724\", \"1.0\"], \"13\": [\"0.9176470637321472\", \"0.8991453249174426\", \"1.0\", \"0.9811549933268298\", \"0.9570742194944619\"], \"14\": [\"0.9537037014961243\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.9126213788986206\", \"0.95590361651259\", \"0.8577341451663933\", \"0.799358726729822\", \"0.809309780504532\"], \"16\": [\"0.942307710647583\", \"0.9506323824318652\", \"1.0\", \"1.0\", \"1.0\"], \"17\": [\"0.8909090757369995\", \"0.9458597618150345\", \"1.0\", \"1.0\", \"0.9593212131992811\"], \"18\": [\"0.9158878326416016\", \"0.8326578128511968\", \"0.8022102546108545\", \"1.0\", \"0.8879098256031821\"], \"19\": [\"0.9621212482452393\", \"1.0\", \"0.9688050743369877\", \"0.9954941235609506\", \"0.9783148891769534\"], \"20\": [\"0.8982300758361816\", \"0.857543273758781\", \"0.904582479269161\", \"0.9808626574410144\", \"0.8590755384678712\"], \"21\": [\"0.9621211886405945\", \"1.0\", \"1.0\", \"1.0\", \"0.9915753120646987\"], \"22\": [\"0.8758620619773865\", \"1.0\", \"1.0\", \"1.0\", \"0.9595588659073383\"], \"23\": [\"0.916967511177063\", \"0.9251057231160493\", \"0.9417636941765279\", \"1.0\", \"0.8515125521376568\"], \"24\": [\"0.9577693343162537\", \"0.9319297591011309\", \"0.9925472053943316\", \"1.0\", \"0.7621692516448988\"]}");
   // get max_n_evals from histories
   var history_data = [];
   for (let i = 0; i < histories[0].length; i++) {
diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html
index ca9bb68dc..47a474a70 100644
--- a/api/tutorials/synthea/los_prediction.html
+++ b/api/tutorials/synthea/los_prediction.html
@@ -566,7 +566,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:06,982 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Database setup, ready to run queries!
+2024-06-23 18:27:50,616 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Database setup, ready to run queries!
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -574,7 +574,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:33,373 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-23 18:28:17,546 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -582,7 +582,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:33,375 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 25.282700 s
+2024-06-23 18:28:17,548 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 25.825068 s
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -590,7 +590,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:38,633 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-23 18:28:22,581 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -598,7 +598,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:38,635 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 5.257180 s
+2024-06-23 18:28:22,583 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 5.031993 s
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -606,7 +606,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:44,560 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-23 18:28:29,007 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -614,7 +614,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:44,562 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 2.159252 s
+2024-06-23 18:28:29,009 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 2.417814 s
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -622,7 +622,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:47,055 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-23 18:28:31,472 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -630,7 +630,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:47,057 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 2.488676 s
+2024-06-23 18:28:31,473 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 2.458872 s
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -638,7 +638,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:47,243 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-23 18:28:31,659 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -646,7 +646,7 @@ <h3>Compute length of stay (labels)<a class="headerlink" href="#Compute-length-o
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-2024-06-22 12:27:47,245 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 0.186659 s
+2024-06-23 18:28:31,661 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 0.185721 s
 </pre></div></div>
 </div>
 </section>
@@ -699,9 +699,9 @@ <h3>Drop NaNs based on the <code class="docutils literal notranslate"><span clas
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area rendered_html docutils container">
-<div>                            <div id="929dc2d4-69cf-4ee3-8216-f1be429c9906" class="plotly-graph-div" style="height:525px; width:100%;"></div>            <script type="text/javascript">                require(["plotly"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("929dc2d4-69cf-4ee3-8216-f1be429c9906")) {                    Plotly.newPlot(                        "929dc2d4-69cf-4ee3-8216-f1be429c9906",                        [{"x":["obs_ALT (Elevated)","obs_AST (Elevated)","obs_Alanine aminotransferase [Enzymatic activity\u002fvolume] in Serum or Plasma","obs_Albumin","obs_Albumin [Mass\u002fvolume] in Serum or Plasma","obs_Alkaline Phosphatase","obs_Alkaline phosphatase [Enzymatic activity\u002fvolume] in Serum or Plasma","obs_Anion Gap","obs_Aspartate aminotransferase [Enzymatic activity\u002fvolume] in Serum or Plasma","obs_Basophils [#\u002fvolume] in Blood by Automated count","obs_Basophils\u002f100 leukocytes in Blood by Automated count","obs_Bicarbonate [Moles\u002fvolume] in Arterial blood","obs_Bilirubin.total [Mass\u002fvolume] in Serum or Plasma","obs_Body Height","obs_Body Mass Index","obs_Body Weight","obs_Body mass index (BMI) [Percentile] Per age and gender","obs_Body temperature","obs_C reactive protein [Mass\u002fvolume] in Serum or Plasma","obs_Calcium","obs_Calcium [Mass\u002fvolume] in Serum or Plasma","obs_Carbon Dioxide","obs_Carbon dioxide  total [Moles\u002fvolume] in Serum or Plasma","obs_Carbon dioxide [Partial pressure] in Arterial blood","obs_Chloride","obs_Chloride [Moles\u002fvolume] in Serum or Plasma","obs_Creatine kinase [Enzymatic activity\u002fvolume] in Serum or Plasma","obs_Creatinine","obs_Creatinine [Mass\u002fvolume] in Serum or Plasma","obs_Diastolic Blood Pressure","obs_Eosinophils [#\u002fvolume] in Blood by Automated count","obs_Eosinophils\u002f100 leukocytes in Blood by Automated count","obs_Erythrocyte distribution width [Entitic volume] by Automated count","obs_Erythrocyte distribution width [Ratio] by Automated count","obs_Erythrocytes [#\u002fvolume] in Blood by Automated count","obs_Estimated Glomerular Filtration Rate","obs_Ferritin [Mass\u002fvolume] in Serum or Plasma","obs_Fibrin D-dimer FEU [Mass\u002fvolume] in Platelet poor plasma","obs_Globulin","obs_Glomerular filtration rate\u002f1.73 sq M.predicted","obs_Glucose","obs_Glucose [Mass\u002fvolume] in Serum or Plasma","obs_Head Occipital-frontal circumference","obs_Heart rate","obs_Hematocrit","obs_Hematocrit [Volume Fraction] of Blood by Automated count","obs_Hemoglobin","obs_Hemoglobin A1c\u002fHemoglobin.total in Blood","obs_Hemoglobin [Mass\u002fvolume] in Blood","obs_High Density Lipoprotein Cholesterol","obs_INR in Platelet poor plasma by Coagulation assay","obs_Interleukin 6 [Mass\u002fvolume] in Serum or Plasma","obs_Iron [Mass\u002fvolume] in Serum or Plasma","obs_Iron binding capacity [Mass\u002fvolume] in Serum 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@@ -1287,7 +1287,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
 </div>
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-2024-06-22 12:36:38,440 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: 0.01
+2024-06-23 18:29:11,735 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
 </pre></div></div>
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@@ -1295,7 +1295,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-22 12:36:38,441 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 2
+2024-06-23 18:29:11,736 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 10
 </pre></div></div>
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@@ -1303,7 +1303,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-22 12:36:38,442 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
+2024-06-23 18:29:11,737 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.8
 </pre></div></div>
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@@ -1716,26 +1716,26 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
   background-color: var(--sklearn-color-fitted-level-3);
 }
 </style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,
-              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,
-              early_stopping_rounds=None, enable_categorical=False,
-              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=2, gpu_id=None,
-              grow_policy=None, importance_type=None,
-              interaction_constraints=None, learning_rate=0.01, max_bin=None,
-              max_cat_threshold=None, max_cat_to_onehot=None,
-              max_delta_step=None, max_depth=5, max_leaves=None,
-              min_child_weight=3, missing=nan, monotone_constraints=None,
-              n_estimators=100, n_jobs=None, num_parallel_tree=None,
-              predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;XGBClassifier<span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,
-              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,
-              early_stopping_rounds=None, enable_categorical=False,
-              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=2, gpu_id=None,
-              grow_policy=None, importance_type=None,
-              interaction_constraints=None, learning_rate=0.01, max_bin=None,
-              max_cat_threshold=None, max_cat_to_onehot=None,
-              max_delta_step=None, max_depth=5, max_leaves=None,
-              min_child_weight=3, missing=nan, monotone_constraints=None,
-              n_estimators=100, n_jobs=None, num_parallel_tree=None,
-              predictor=None, random_state=123, ...)</pre></div> </div></div></div></div></div>
+              colsample_bylevel=None, colsample_bynode=None,
+              colsample_bytree=0.8, early_stopping_rounds=None,
+              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,
+              feature_types=None, gamma=10, gpu_id=None, grow_policy=None,
+              importance_type=None, interaction_constraints=None,
+              learning_rate=0.1, max_bin=None, max_cat_threshold=None,
+              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,
+              max_leaves=None, min_child_weight=3, missing=nan,
+              monotone_constraints=None, n_estimators=500, n_jobs=None,
+              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;XGBClassifier<span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,
+              colsample_bylevel=None, colsample_bynode=None,
+              colsample_bytree=0.8, early_stopping_rounds=None,
+              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,
+              feature_types=None, gamma=10, gpu_id=None, grow_policy=None,
+              importance_type=None, interaction_constraints=None,
+              learning_rate=0.1, max_bin=None, max_cat_threshold=None,
+              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,
+              max_leaves=None, min_child_weight=3, missing=nan,
+              monotone_constraints=None, n_estimators=500, n_jobs=None,
+              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre></div> </div></div></div></div></div>
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@@ -1751,7 +1751,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-{&#39;objective&#39;: &#39;binary:logistic&#39;, &#39;use_label_encoder&#39;: None, &#39;base_score&#39;: None, &#39;booster&#39;: None, &#39;callbacks&#39;: None, &#39;colsample_bylevel&#39;: None, &#39;colsample_bynode&#39;: None, &#39;colsample_bytree&#39;: 1, &#39;early_stopping_rounds&#39;: None, &#39;enable_categorical&#39;: False, &#39;eval_metric&#39;: &#39;logloss&#39;, &#39;feature_types&#39;: None, &#39;gamma&#39;: 2, &#39;gpu_id&#39;: None, &#39;grow_policy&#39;: None, &#39;importance_type&#39;: None, &#39;interaction_constraints&#39;: None, &#39;learning_rate&#39;: 0.01, &#39;max_bin&#39;: None, &#39;max_cat_threshold&#39;: None, &#39;max_cat_to_onehot&#39;: None, &#39;max_delta_step&#39;: None, &#39;max_depth&#39;: 5, &#39;max_leaves&#39;: None, &#39;min_child_weight&#39;: 3, &#39;missing&#39;: nan, &#39;monotone_constraints&#39;: None, &#39;n_estimators&#39;: 100, &#39;n_jobs&#39;: None, &#39;num_parallel_tree&#39;: None, &#39;predictor&#39;: None, &#39;random_state&#39;: 123, &#39;reg_alpha&#39;: None, &#39;reg_lambda&#39;: 1, &#39;sampling_method&#39;: None, &#39;scale_pos_weight&#39;: None, &#39;subsample&#39;: None, &#39;tree_method&#39;: None, &#39;validate_parameters&#39;: None, &#39;verbosity&#39;: None, &#39;seed&#39;: 123}
+{&#39;objective&#39;: &#39;binary:logistic&#39;, &#39;use_label_encoder&#39;: None, &#39;base_score&#39;: None, &#39;booster&#39;: None, &#39;callbacks&#39;: None, &#39;colsample_bylevel&#39;: None, &#39;colsample_bynode&#39;: None, &#39;colsample_bytree&#39;: 0.8, &#39;early_stopping_rounds&#39;: None, &#39;enable_categorical&#39;: False, &#39;eval_metric&#39;: &#39;logloss&#39;, &#39;feature_types&#39;: None, &#39;gamma&#39;: 10, &#39;gpu_id&#39;: None, &#39;grow_policy&#39;: None, &#39;importance_type&#39;: None, &#39;interaction_constraints&#39;: None, &#39;learning_rate&#39;: 0.1, &#39;max_bin&#39;: None, &#39;max_cat_threshold&#39;: None, &#39;max_cat_to_onehot&#39;: None, &#39;max_delta_step&#39;: None, &#39;max_depth&#39;: 5, &#39;max_leaves&#39;: None, &#39;min_child_weight&#39;: 3, &#39;missing&#39;: nan, &#39;monotone_constraints&#39;: None, &#39;n_estimators&#39;: 500, &#39;n_jobs&#39;: None, &#39;num_parallel_tree&#39;: None, &#39;predictor&#39;: None, &#39;random_state&#39;: 123, &#39;reg_alpha&#39;: None, &#39;reg_lambda&#39;: 0, &#39;sampling_method&#39;: None, &#39;scale_pos_weight&#39;: None, &#39;subsample&#39;: None, &#39;tree_method&#39;: None, &#39;validate_parameters&#39;: None, &#39;verbosity&#39;: None, &#39;seed&#39;: 123}
 </pre></div></div>
 </div>
 <p><strong>Log the model parameters to the report.</strong></p>
@@ -1787,7 +1787,7 @@ <h2>Prediction<a class="headerlink" href="#Prediction" title="Link to this headi
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "6efe382d4fb8423499df13036133999f"}</script></div>
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@@ -1914,91 +1914,91 @@ <h2>Evaluation<a class="headerlink" href="#Evaluation" title="Link to this headi
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "23eee62f26b4447e8832d61de36fc130"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "0d9fa0369f6348e89ffb92b0f869a374"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "11c74a7ac1424e7fb0d5ce412eec2a23"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "ee0080539eb349a1a8c9982ec9229b00"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "b10c3da39f524b429b40269986db121d"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "3bbba8ba9dd344d7a0174a71c8771726"}</script></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>
@@ -2101,9 +2101,9 @@ <h2>Evaluation<a class="headerlink" href="#Evaluation" title="Link to this headi
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diff --git a/api/tutorials/synthea/los_prediction.ipynb b/api/tutorials/synthea/los_prediction.ipynb
index a5c41c92f..8331d4fb2 100644
--- a/api/tutorials/synthea/los_prediction.ipynb
+++ b/api/tutorials/synthea/los_prediction.ipynb
@@ -33,10 +33,10 @@
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@@ -97,10 +97,10 @@
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@@ -123,10 +123,10 @@
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      "text": [
-      "2024-06-22 12:27:06,982 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Database setup, ready to run queries!\n"
+      "2024-06-23 18:27:50,616 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Database setup, ready to run queries!\n"
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-      "2024-06-22 12:27:33,375 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 25.282700 s\n"
+      "2024-06-23 18:28:17,548 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 25.825068 s\n"
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+      "2024-06-23 18:28:22,581 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
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-      "2024-06-22 12:27:44,562 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 2.159252 s\n"
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-      "2024-06-22 12:27:47,055 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
+      "2024-06-23 18:28:31,472 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
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      "output_type": "stream",
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-      "2024-06-22 12:27:47,057 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 2.488676 s\n"
+      "2024-06-23 18:28:31,473 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 2.458872 s\n"
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-      "2024-06-22 12:27:47,243 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
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+      "2024-06-23 18:28:31,661 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.185721 s\n"
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@@ -1579,10 +1579,10 @@
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@@ -1639,8 +1639,8 @@
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@@ -2500,9 +2500,9 @@
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@@ -2588,10 +2588,10 @@
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@@ -8870,10 +8870,10 @@
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-     "shell.execute_reply": "2024-06-22T16:27:48.903220Z"
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@@ -8882,7 +8882,7 @@
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-      "{'obs_Hematocrit [Volume Fraction] of Blood by Automated count': 'numeric', 'n_meds': 'ordinal', 'obs_Sodium [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Bilirubin.total [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Body Weight': 'numeric', 'obs_Diastolic Blood Pressure': 'numeric', 'obs_Glomerular filtration rate/1.73 sq M.predicted': 'numeric', 'obs_Platelets [#/volume] in Blood by Automated count': 'numeric', 'obs_Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'gender': 'binary', 'outcome': 'binary', 'obs_Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method': 'numeric', 'obs_Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Erythrocytes [#/volume] in Blood by Automated count': 'numeric', 'obs_Creatinine [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Oxygen saturation in Arterial blood': 'numeric', 'obs_Glucose [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Urea nitrogen [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Systolic Blood Pressure': 'numeric', 'obs_Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Albumin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Calcium [Mass/volume] in Serum or Plasma': 'numeric', 'obs_MCV [Entitic volume] by Automated count': 'numeric', 'obs_Chloride [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Carbon dioxide  total [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Potassium [Moles/volume] in Serum or Plasma': 'numeric', 'n_procedures': 'numeric', 'obs_Ferritin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Leukocytes [#/volume] in Blood by Automated count': 'numeric', 'age': 'numeric', 'obs_Hemoglobin [Mass/volume] in Blood': 'numeric', 'n_obs': 'numeric', 'obs_MCHC [Mass/volume] by Automated count': 'numeric', 'obs_MCH [Entitic mass] by Automated count': 'numeric', 'obs_Protein [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Erythrocyte distribution width [Ratio] by Automated count': 'numeric'}\n"
+      "{'age': 'numeric', 'obs_Diastolic Blood Pressure': 'numeric', 'obs_Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_MCV [Entitic volume] by Automated count': 'numeric', 'gender': 'binary', 'n_procedures': 'numeric', 'obs_Leukocytes [#/volume] in Blood by Automated count': 'numeric', 'obs_Albumin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Oxygen saturation in Arterial blood': 'numeric', 'obs_Glomerular filtration rate/1.73 sq M.predicted': 'numeric', 'obs_Erythrocytes [#/volume] in Blood by Automated count': 'numeric', 'obs_Bilirubin.total [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Protein [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Potassium [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Sodium [Moles/volume] in Serum or Plasma': 'numeric', 'n_obs': 'numeric', 'obs_MCHC [Mass/volume] by Automated count': 'numeric', 'obs_Platelets [#/volume] in Blood by Automated count': 'numeric', 'n_meds': 'ordinal', 'obs_Calcium [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Hematocrit [Volume Fraction] of Blood by Automated count': 'numeric', 'obs_MCH [Entitic mass] by Automated count': 'numeric', 'obs_Carbon dioxide  total [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Systolic Blood Pressure': 'numeric', 'obs_Hemoglobin [Mass/volume] in Blood': 'numeric', 'obs_Erythrocyte distribution width [Ratio] by Automated count': 'numeric', 'obs_Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method': 'numeric', 'obs_Urea nitrogen [Mass/volume] in Serum or Plasma': 'numeric', 'outcome': 'binary', 'obs_Body Weight': 'numeric', 'obs_Creatinine [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Chloride [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Ferritin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Glucose [Mass/volume] in Serum or Plasma': 'numeric'}\n"
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@@ -8950,10 +8950,10 @@
    "id": "2bb80cfb-2e48-4ecf-a325-0026dad4aef7",
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-     "iopub.execute_input": "2024-06-22T16:27:48.912685Z",
-     "iopub.status.busy": "2024-06-22T16:27:48.911436Z",
-     "iopub.status.idle": "2024-06-22T16:27:48.920878Z",
-     "shell.execute_reply": "2024-06-22T16:27:48.919421Z"
+     "iopub.execute_input": "2024-06-23T22:28:33.318817Z",
+     "iopub.status.busy": "2024-06-23T22:28:33.317455Z",
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@@ -8974,10 +8974,10 @@
    "id": "31ec3441-7dba-4b9a-9256-ad6b0293829d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T16:27:48.928067Z",
-     "iopub.status.busy": "2024-06-22T16:27:48.926748Z",
-     "iopub.status.idle": "2024-06-22T16:27:49.040707Z",
-     "shell.execute_reply": "2024-06-22T16:27:49.034792Z"
+     "iopub.execute_input": "2024-06-23T22:28:33.333220Z",
+     "iopub.status.busy": "2024-06-23T22:28:33.332262Z",
+     "iopub.status.idle": "2024-06-23T22:28:33.438800Z",
+     "shell.execute_reply": "2024-06-23T22:28:33.437082Z"
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@@ -9004,10 +9004,10 @@
    "id": "f2fb5345-17f9-44ac-b944-079324a098e4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T16:27:49.048122Z",
-     "iopub.status.busy": "2024-06-22T16:27:49.046817Z",
-     "iopub.status.idle": "2024-06-22T16:27:49.063898Z",
-     "shell.execute_reply": "2024-06-22T16:27:49.062130Z"
+     "iopub.execute_input": "2024-06-23T22:28:33.446181Z",
+     "iopub.status.busy": "2024-06-23T22:28:33.444858Z",
+     "iopub.status.idle": "2024-06-23T22:28:33.458309Z",
+     "shell.execute_reply": "2024-06-23T22:28:33.456740Z"
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@@ -9035,10 +9035,10 @@
    "id": "868fa57c-f12a-4821-a85e-31c6c95d6e76",
    "metadata": {
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-     "iopub.execute_input": "2024-06-22T16:27:49.070413Z",
-     "iopub.status.busy": "2024-06-22T16:27:49.069752Z",
-     "iopub.status.idle": "2024-06-22T16:27:49.083015Z",
-     "shell.execute_reply": "2024-06-22T16:27:49.077207Z"
+     "iopub.execute_input": "2024-06-23T22:28:33.464916Z",
+     "iopub.status.busy": "2024-06-23T22:28:33.463974Z",
+     "iopub.status.idle": "2024-06-23T22:28:33.472463Z",
+     "shell.execute_reply": "2024-06-23T22:28:33.470842Z"
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@@ -9069,10 +9069,10 @@
    "id": "87b72a66-fc46-4e7a-a612-1f66de811c0d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T16:27:49.098517Z",
-     "iopub.status.busy": "2024-06-22T16:27:49.097537Z",
-     "iopub.status.idle": "2024-06-22T16:27:49.247339Z",
-     "shell.execute_reply": "2024-06-22T16:27:49.245844Z"
+     "iopub.execute_input": "2024-06-23T22:28:33.479159Z",
+     "iopub.status.busy": "2024-06-23T22:28:33.478185Z",
+     "iopub.status.idle": "2024-06-23T22:28:33.621863Z",
+     "shell.execute_reply": "2024-06-23T22:28:33.620150Z"
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@@ -9100,10 +9100,10 @@
    "id": "c6370e7a-c559-431f-9ebc-0b35bcdfb029",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T16:27:49.253441Z",
-     "iopub.status.busy": "2024-06-22T16:27:49.252849Z",
-     "iopub.status.idle": "2024-06-22T16:27:49.404344Z",
-     "shell.execute_reply": "2024-06-22T16:27:49.402726Z"
+     "iopub.execute_input": "2024-06-23T22:28:33.629922Z",
+     "iopub.status.busy": "2024-06-23T22:28:33.628592Z",
+     "iopub.status.idle": "2024-06-23T22:28:33.759192Z",
+     "shell.execute_reply": "2024-06-23T22:28:33.757545Z"
     },
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@@ -9111,7 +9111,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4139c3ace09149b39312337f2eb82477",
+       "model_id": "a67f1652e5a44acda3c22a02a9b3e865",
        "version_major": 2,
        "version_minor": 0
       },
@@ -9148,10 +9148,10 @@
    "id": "42e8d147-545c-47a7-9a91-b06b27a37d47",
    "metadata": {
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-     "iopub.execute_input": "2024-06-22T16:27:49.412279Z",
-     "iopub.status.busy": "2024-06-22T16:27:49.411200Z",
-     "iopub.status.idle": "2024-06-22T16:27:52.541510Z",
-     "shell.execute_reply": "2024-06-22T16:27:52.539899Z"
+     "iopub.execute_input": "2024-06-23T22:28:33.767203Z",
+     "iopub.status.busy": "2024-06-23T22:28:33.765875Z",
+     "iopub.status.idle": "2024-06-23T22:28:36.878875Z",
+     "shell.execute_reply": "2024-06-23T22:28:36.877169Z"
     },
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@@ -9177,10 +9177,10 @@
    "id": "01d36851-292a-4849-98e0-9183c2dec87a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T16:27:52.549460Z",
-     "iopub.status.busy": "2024-06-22T16:27:52.548489Z",
-     "iopub.status.idle": "2024-06-22T16:27:52.560949Z",
-     "shell.execute_reply": "2024-06-22T16:27:52.559533Z"
+     "iopub.execute_input": "2024-06-23T22:28:36.887392Z",
+     "iopub.status.busy": "2024-06-23T22:28:36.886014Z",
+     "iopub.status.idle": "2024-06-23T22:28:36.897767Z",
+     "shell.execute_reply": "2024-06-23T22:28:36.896208Z"
     },
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@@ -9223,10 +9223,10 @@
    "id": "572b0fe0-5891-4aca-9e63-8d556851caec",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T16:27:52.571394Z",
-     "iopub.status.busy": "2024-06-22T16:27:52.566871Z",
-     "iopub.status.idle": "2024-06-22T16:36:38.510165Z",
-     "shell.execute_reply": "2024-06-22T16:36:38.508377Z"
+     "iopub.execute_input": "2024-06-23T22:28:36.904473Z",
+     "iopub.status.busy": "2024-06-23T22:28:36.903544Z",
+     "iopub.status.idle": "2024-06-23T22:29:11.776136Z",
+     "shell.execute_reply": "2024-06-23T22:29:11.774487Z"
     },
     "tags": []
    },
@@ -9235,42 +9235,42 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 12:36:38,435 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 1\n"
+      "2024-06-23 18:29:11,730 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 12:36:38,437 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 100\n"
+      "2024-06-23 18:29:11,732 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 500\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 12:36:38,438 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
+      "2024-06-23 18:29:11,734 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 12:36:38,440 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.01\n"
+      "2024-06-23 18:29:11,735 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 12:36:38,441 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 2\n"
+      "2024-06-23 18:29:11,736 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 10\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-22 12:36:38,442 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 1\n"
+      "2024-06-23 18:29:11,737 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.8\n"
      ]
     },
     {
@@ -9681,39 +9681,39 @@
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,\n",
-       "              early_stopping_rounds=None, enable_categorical=False,\n",
-       "              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=2, gpu_id=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
-       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,\n",
-       "              early_stopping_rounds=None, enable_categorical=False,\n",
-       "              eval_metric=&#x27;logloss&#x27;, feature_types=None, gamma=2, gpu_id=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
-       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=123, ...)</pre></div> </div></div></div></div>"
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=0.8, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
+       "              feature_types=None, gamma=10, gpu_id=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=500, n_jobs=None,\n",
+       "              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=0.8, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
+       "              feature_types=None, gamma=10, gpu_id=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=500, n_jobs=None,\n",
+       "              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None, colsample_bytree=1,\n",
-       "              early_stopping_rounds=None, enable_categorical=False,\n",
-       "              eval_metric='logloss', feature_types=None, gamma=2, gpu_id=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
-       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=123, ...)"
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=0.8, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric='logloss',\n",
+       "              feature_types=None, gamma=10, gpu_id=None, grow_policy=None,\n",
+       "              importance_type=None, interaction_constraints=None,\n",
+       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=5,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=500, n_jobs=None,\n",
+       "              num_parallel_tree=None, predictor=None, random_state=123, ...)"
       ]
      },
      "execution_count": 26,
@@ -9745,10 +9745,10 @@
    "id": "9b687a58-a6e5-46f7-bd9e-106376912086",
    "metadata": {
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-     "iopub.status.busy": "2024-06-22T16:36:38.516394Z",
-     "iopub.status.idle": "2024-06-22T16:36:38.526187Z",
-     "shell.execute_reply": "2024-06-22T16:36:38.524626Z"
+     "iopub.execute_input": "2024-06-23T22:29:11.783380Z",
+     "iopub.status.busy": "2024-06-23T22:29:11.782109Z",
+     "iopub.status.idle": "2024-06-23T22:29:11.791879Z",
+     "shell.execute_reply": "2024-06-23T22:29:11.790271Z"
     },
     "tags": []
    },
@@ -9757,7 +9757,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 2, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 100, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 1, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}\n"
+      "{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'gamma': 10, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.1, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 5, 'max_leaves': None, 'min_child_weight': 3, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 500, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': 0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}\n"
      ]
     }
    ],
@@ -9782,10 +9782,10 @@
    "id": "4cfdfe29-c813-45cd-9ae7-2959daa0cd8f",
    "metadata": {
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-     "iopub.execute_input": "2024-06-22T16:36:38.533076Z",
-     "iopub.status.busy": "2024-06-22T16:36:38.532053Z",
-     "iopub.status.idle": "2024-06-22T16:36:38.540504Z",
-     "shell.execute_reply": "2024-06-22T16:36:38.538962Z"
+     "iopub.execute_input": "2024-06-23T22:29:11.798587Z",
+     "iopub.status.busy": "2024-06-23T22:29:11.797666Z",
+     "iopub.status.idle": "2024-06-23T22:29:11.805921Z",
+     "shell.execute_reply": "2024-06-23T22:29:11.804376Z"
     }
    },
    "outputs": [],
@@ -9809,10 +9809,10 @@
    "id": "c3fee35c-902e-4c54-89b7-c42c1e935e1b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-22T16:36:38.547317Z",
-     "iopub.status.busy": "2024-06-22T16:36:38.546288Z",
-     "iopub.status.idle": "2024-06-22T16:36:38.844757Z",
-     "shell.execute_reply": "2024-06-22T16:36:38.842886Z"
+     "iopub.execute_input": "2024-06-23T22:29:11.812529Z",
+     "iopub.status.busy": "2024-06-23T22:29:11.811625Z",
+     "iopub.status.idle": "2024-06-23T22:29:12.119304Z",
+     "shell.execute_reply": "2024-06-23T22:29:12.117607Z"
     },
     "tags": []
    },
@@ -9820,7 +9820,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "655a931d39c949faaa6ebebc9273761a",
+       "model_id": "6efe382d4fb8423499df13036133999f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -9868,10 +9868,10 @@
    "id": "d3d5b101-17a8-4610-ae17-e04cf962d2d1",
    "metadata": {
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@@ -12611,10 +12607,10 @@
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-     "shell.execute_reply": "2024-06-22T16:36:41.334248Z"
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@@ -12634,10 +12630,10 @@
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-     "iopub.status.idle": "2024-06-22T16:36:41.399009Z",
-     "shell.execute_reply": "2024-06-22T16:36:41.397059Z"
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+     "iopub.status.busy": "2024-06-23T22:29:14.368833Z",
+     "iopub.status.idle": "2024-06-23T22:29:14.425116Z",
+     "shell.execute_reply": "2024-06-23T22:29:14.423470Z"
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    "outputs": [
@@ -12658,11 +12654,11 @@
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@@ -13499,9 +13495,9 @@
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@@ -13550,10 +13546,10 @@
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-     "iopub.status.idle": "2024-06-22T16:36:41.415400Z",
-     "shell.execute_reply": "2024-06-22T16:36:41.413724Z"
+     "iopub.execute_input": "2024-06-23T22:29:14.432158Z",
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+     "iopub.status.idle": "2024-06-23T22:29:14.440674Z",
+     "shell.execute_reply": "2024-06-23T22:29:14.439023Z"
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    },
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@@ -13576,10 +13572,10 @@
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+     "shell.execute_reply": "2024-06-23T22:29:14.617193Z"
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