From 5fd8749841a61232b9577bd8d489e2b3fd38c8c5 Mon Sep 17 00:00:00 2001
From: amrit110 <amrit110@users.noreply.github.com>
Date: Mon, 10 Jun 2024 20:52:20 -0400
Subject: [PATCH] deploy: c511b1bc999daabd207e8aa3711d81c53618b167

---
 .../heart_failure_report_periodic.html        |    32 +-
 api/examples/metrics.html                     |   194 +-
 api/examples/metrics.ipynb                    | 14292 ++--
 api/examples/report.html                      |    96 +-
 api/examples/report.ipynb                     |  1082 +-
 api/searchindex.js                            |     2 +-
 .../diabetes_130/readmission_prediction.html  |   134 +-
 .../diabetes_130/readmission_prediction.ipynb |  9940 +--
 .../kaggle/heart_failure_prediction.html      |    86 +-
 .../kaggle/heart_failure_prediction.ipynb     |  3994 +-
 .../kaggle/heart_failure_report_periodic.html |   154 +-
 .../mimiciv/mortality_prediction.html         |   138 +-
 .../mimiciv/mortality_prediction.ipynb        | 62260 ++++++++--------
 .../mimiciv/mortality_report_periodic.html    |   146 +-
 api/tutorials/nihcxr/cxr_classification.html  |    44 +-
 api/tutorials/nihcxr/cxr_classification.ipynb |  3581 +-
 .../nihcxr/nihcxr_report_periodic.html        |     6 +-
 .../length_of_stay_report_periodic.html       |   138 +-
 api/tutorials/synthea/los_prediction.html     |   154 +-
 api/tutorials/synthea/los_prediction.ipynb    |  8663 ++-
 20 files changed, 53430 insertions(+), 51706 deletions(-)

diff --git a/api/examples/heart_failure_report_periodic.html b/api/examples/heart_failure_report_periodic.html
index b8f97ed62..2c58f8e5b 100644
--- a/api/examples/heart_failure_report_periodic.html
+++ b/api/examples/heart_failure_report_periodic.html
@@ -1051,7 +1051,7 @@ <h3>Citation</h3>
   
     
     <div class="subcard">
-      <h3>Reference</h3>
+      <h3>License</h3>
       <ul>
       
 <div>
@@ -1061,11 +1061,17 @@ <h3>Reference</h3>
       
         
           
-            <a href="https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction">https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction</a>
+            
+              <b>Identifier:</b> CC0-1.0
+            
           
           <br>
         
       
+        
+      
+        
+      
     </li>
     
   
@@ -1079,7 +1085,7 @@ <h3>Reference</h3>
   
     
     <div class="subcard">
-      <h3>License</h3>
+      <h3>Reference</h3>
       <ul>
       
 <div>
@@ -1089,17 +1095,11 @@ <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>
     
   
@@ -1126,7 +1126,7 @@ <h3>Graphics</h3>
     
 <div class="img-item">
   
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@@ -1376,7 +1376,7 @@ <h3>Graphics</h3>
     
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@@ -1442,7 +1442,7 @@ <h3>Version</h3>
         
           
             
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diff --git a/api/examples/metrics.ipynb b/api/examples/metrics.ipynb
index d76ef72c8..6fe24736c 100644
--- a/api/examples/metrics.ipynb
+++ b/api/examples/metrics.ipynb
@@ -14,10 +14,10 @@
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@@ -767,10 +767,10 @@
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@@ -824,17 +824,17 @@
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-      "2024-06-04 00:06:08,484 \u001b[1;33mWARNING\u001b[0m cyclops.evaluate.fairness.evaluator - The number of unique values for the group is greater than 50. This may take a long time to compute. Consider binning the values into fewer groups.\n"
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diff --git a/api/examples/report.ipynb b/api/examples/report.ipynb
index 0c49c3657..b6a032fb6 100644
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@@ -74,10 +74,10 @@
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@@ -100,10 +100,10 @@
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@@ -275,10 +275,10 @@
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@@ -2312,10 +2312,10 @@
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@@ -4173,10 +4173,10 @@
    "execution_count": 10,
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-     "shell.execute_reply": "2024-06-04T04:06:36.534120Z"
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@@ -4190,10 +4190,10 @@
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@@ -5972,9 +5972,9 @@
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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": "2024-06-04T04:06:38.505636Z",
-     "shell.execute_reply": "2024-06-04T04:06:38.504062Z"
+     "iopub.execute_input": "2024-06-10T23:48:13.533815Z",
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@@ -12039,9 +12039,9 @@
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"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 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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.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"]], 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\ No newline at end of file
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"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, 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"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
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--- a/api/tutorials/diabetes_130/readmission_prediction.html
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@@ -372,7 +372,6 @@ <h2>Install libraries<a class="headerlink" href="#Install-libraries" title="Link
 Requirement already satisfied: pybtex&lt;0.25.0,&gt;=0.24.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_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-1/_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-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.4.0)
-Requirement already satisfied: scipy&lt;2.0.0,&gt;=1.11.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.13.0rc1)
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+Requirement already satisfied: scipy&lt;2.0.0,&gt;=1.11.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.13.0rc1)
 Requirement already satisfied: scour&lt;0.39.0,&gt;=0.38.2 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (0.38.2)
 Requirement already satisfied: spdx-tools&lt;0.9.0,&gt;=0.8.1 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (0.8.2)
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@@ -401,16 +401,8 @@ <h2>Install libraries<a class="headerlink" href="#Install-libraries" title="Link
 Requirement already satisfied: huggingface-hub&gt;=0.21.2 in /mnt/data/actions_runners/cyclops-actions-runner-1/_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-1/_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-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets&lt;3.0.0,&gt;=2.15.0-&gt;pycyclops) (6.0.1)
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 Requirement already satisfied: omegaconf&lt;2.4,&gt;=2.2 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from hydra-core&lt;2.0.0,&gt;=1.2.0-&gt;pycyclops) (2.3.0)
 Requirement already satisfied: antlr4-python3-runtime==4.9.* in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from hydra-core&lt;2.0.0,&gt;=1.2.0-&gt;pycyclops) (4.9.3)
-Requirement already satisfied: MarkupSafe&gt;=2.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from Jinja2&lt;4.0.0,&gt;=3.1.3-&gt;pycyclops) (2.1.3)
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@@ -418,6 +410,7 @@ <h2>Install libraries<a class="headerlink" href="#Install-libraries" title="Link
 </div>
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 <div class="highlight"><pre>
+Requirement already satisfied: MarkupSafe&gt;=2.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from Jinja2&lt;4.0.0,&gt;=3.1.3-&gt;pycyclops) (2.1.3)
 Requirement already satisfied: contourpy&gt;=1.0.1 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from matplotlib&lt;4.0.0,&gt;=3.8.3-&gt;pycyclops) (1.1.0)
 Requirement already satisfied: cycler&gt;=0.10 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from matplotlib&lt;4.0.0,&gt;=3.8.3-&gt;pycyclops) (0.12.1)
 Requirement already satisfied: fonttools&gt;=4.22.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from matplotlib&lt;4.0.0,&gt;=3.8.3-&gt;pycyclops) (4.47.0)
@@ -460,6 +453,13 @@ <h2>Install libraries<a class="headerlink" href="#Install-libraries" title="Link
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 Requirement already satisfied: latexcodec&gt;=1.0.4 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pybtex&lt;0.25.0,&gt;=0.24.0-&gt;pycyclops) (2.0.1)
 Requirement already satisfied: six in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pybtex&lt;0.25.0,&gt;=0.24.0-&gt;pycyclops) (1.16.0)
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 Requirement already satisfied: typing-extensions&gt;=4.2.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pydantic&lt;2.0.0,&gt;=1.10.11-&gt;pycyclops) (4.9.0)
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-{&#39;number_diagnoses&#39;: &#39;ordinal&#39;, &#39;time_in_hospital&#39;: &#39;ordinal&#39;, &#39;medical_specialty&#39;: &#39;string&#39;, &#39;change&#39;: &#39;binary&#39;, &#39;discharge_disposition_id&#39;: &#39;ordinal&#39;, &#39;metformin&#39;: &#39;ordinal&#39;, &#39;num_procedures&#39;: &#39;ordinal&#39;, &#39;diag_2&#39;: &#39;string&#39;, &#39;diabetesMed&#39;: &#39;binary&#39;, &#39;diag_1&#39;: &#39;string&#39;, &#39;number_outpatient&#39;: &#39;ordinal&#39;, &#39;glipizide&#39;: &#39;ordinal&#39;, &#39;admission_type_id&#39;: &#39;ordinal&#39;, &#39;num_lab_procedures&#39;: &#39;numeric&#39;, &#39;num_medications&#39;: &#39;numeric&#39;, &#39;pioglitazone&#39;: &#39;ordinal&#39;, &#39;acarbose&#39;: &#39;binary&#39;, &#39;tolbutamide&#39;: &#39;binary&#39;, &#39;max_glu_serum&#39;: &#39;ordinal&#39;, &#39;rosiglitazone&#39;: &#39;ordinal&#39;, &#39;glimepiride&#39;: &#39;ordinal&#39;, &#39;number_emergency&#39;: &#39;ordinal&#39;, &#39;outcome&#39;: &#39;binary&#39;, &#39;A1Cresult&#39;: &#39;ordinal&#39;, &#39;repaglinide&#39;: &#39;ordinal&#39;, &#39;number_inpatient&#39;: &#39;ordinal&#39;, &#39;admission_source_id&#39;: &#39;ordinal&#39;, &#39;tolazamide&#39;: &#39;binary&#39;, &#39;gender&#39;: &#39;binary&#39;, &#39;diag_3&#39;: &#39;string&#39;, &#39;glyburide&#39;: &#39;ordinal&#39;, &#39;troglitazone&#39;: &#39;binary&#39;, &#39;age&#39;: &#39;ordinal&#39;, &#39;insulin&#39;: &#39;ordinal&#39;, &#39;race&#39;: &#39;ordinal&#39;}
+{&#39;admission_type_id&#39;: &#39;ordinal&#39;, &#39;change&#39;: &#39;binary&#39;, &#39;time_in_hospital&#39;: &#39;ordinal&#39;, &#39;insulin&#39;: &#39;ordinal&#39;, &#39;metformin&#39;: &#39;ordinal&#39;, &#39;number_diagnoses&#39;: &#39;ordinal&#39;, &#39;diag_1&#39;: &#39;string&#39;, &#39;troglitazone&#39;: &#39;binary&#39;, &#39;diag_2&#39;: &#39;string&#39;, &#39;diag_3&#39;: &#39;string&#39;, &#39;glipizide&#39;: &#39;ordinal&#39;, &#39;tolazamide&#39;: &#39;binary&#39;, &#39;diabetesMed&#39;: &#39;binary&#39;, &#39;tolbutamide&#39;: &#39;binary&#39;, &#39;rosiglitazone&#39;: &#39;ordinal&#39;, &#39;A1Cresult&#39;: &#39;ordinal&#39;, &#39;glyburide&#39;: &#39;ordinal&#39;, &#39;medical_specialty&#39;: &#39;string&#39;, &#39;num_medications&#39;: &#39;numeric&#39;, &#39;num_lab_procedures&#39;: &#39;numeric&#39;, &#39;discharge_disposition_id&#39;: &#39;ordinal&#39;, &#39;age&#39;: &#39;ordinal&#39;, &#39;outcome&#39;: &#39;binary&#39;, &#39;gender&#39;: &#39;binary&#39;, &#39;max_glu_serum&#39;: &#39;ordinal&#39;, &#39;repaglinide&#39;: &#39;ordinal&#39;, &#39;number_emergency&#39;: &#39;ordinal&#39;, &#39;admission_source_id&#39;: &#39;ordinal&#39;, &#39;number_inpatient&#39;: &#39;ordinal&#39;, &#39;pioglitazone&#39;: &#39;ordinal&#39;, &#39;race&#39;: &#39;ordinal&#39;, &#39;glimepiride&#39;: &#39;ordinal&#39;, &#39;num_procedures&#39;: &#39;ordinal&#39;, &#39;number_outpatient&#39;: &#39;ordinal&#39;, &#39;acarbose&#39;: &#39;binary&#39;}
 </pre></div></div>
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@@ -1188,7 +1188,7 @@ <h2>Creating Hugging Face Dataset<a class="headerlink" href="#Creating-Hugging-F
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@@ -1269,7 +1269,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-04 00:14:27,608 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best scale_pos_weight: 9
+2024-06-10 19:50:08,481 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best scale_pos_weight: 9
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-2024-06-04 00:14:27,713 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best reg_lambda: 1
+2024-06-10 19:50:08,486 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best reg_lambda: 1
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-2024-06-04 00:14:27,714 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best n_estimators: 500
+2024-06-10 19:50:08,487 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best n_estimators: 250
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-2024-06-04 00:14:27,717 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best max_depth: 5
+2024-06-10 19:50:08,489 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best max_depth: 5
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@@ -1301,7 +1301,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-04 00:14:27,719 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2024-06-10 19:50:08,490 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
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@@ -1309,7 +1309,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-04 00:14:27,721 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 2
+2024-06-10 19:50:08,491 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 0
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-2024-06-04 00:14:27,723 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.8
+2024-06-10 19:50:08,493 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.8
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@@ -1733,22 +1733,22 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
               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=2, gpu_id=None, grow_policy=None,
+              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,
+              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=0.8, early_stopping_rounds=None,
               enable_categorical=False, eval_metric=&#x27;logloss&#x27;,
-              feature_types=None, gamma=2, gpu_id=None, grow_policy=None,
+              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,
+              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>
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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;: 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;: 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;: 1, &#39;sampling_method&#39;: None, &#39;scale_pos_weight&#39;: 9, &#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;: 0, &#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;: 1, &#39;sampling_method&#39;: None, &#39;scale_pos_weight&#39;: 9, &#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>
 <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 390f789f0..10c2b6cfb 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": "2024-06-04T04:07:45.786636Z"
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@@ -56,14 +56,14 @@
       "Requirement already satisfied: pyarrow<15.0.0,>=14.0.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (14.0.2)\r\n",
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-      "Requirement already satisfied: scikit-learn<2.0.0,>=1.4.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.4.0)\r\n",
-      "Requirement already satisfied: scipy<2.0.0,>=1.11.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.13.0rc1)\r\n"
+      "Requirement already satisfied: scikit-learn<2.0.0,>=1.4.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.4.0)\r\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Requirement already satisfied: scipy<2.0.0,>=1.11.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (1.13.0rc1)\r\n",
       "Requirement already satisfied: scour<0.39.0,>=0.38.2 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pycyclops) (0.38.2)\r\n",
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      ]
@@ -83,22 +83,16 @@
       "Requirement already satisfied: aiohttp in /mnt/data/actions_runners/cyclops-actions-runner-1/_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-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets<3.0.0,>=2.15.0->pycyclops) (0.22.2)\r\n",
       "Requirement already satisfied: packaging in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets<3.0.0,>=2.15.0->pycyclops) (23.2)\r\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets<3.0.0,>=2.15.0->pycyclops) (6.0.1)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Requirement already satisfied: pyyaml>=5.1 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from datasets<3.0.0,>=2.15.0->pycyclops) (6.0.1)\r\n",
       "Requirement already satisfied: omegaconf<2.4,>=2.2 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from hydra-core<2.0.0,>=1.2.0->pycyclops) (2.3.0)\r\n",
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-      "Requirement already satisfied: MarkupSafe>=2.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from Jinja2<4.0.0,>=3.1.3->pycyclops) (2.1.3)\r\n"
+      "Requirement already satisfied: antlr4-python3-runtime==4.9.* in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from hydra-core<2.0.0,>=1.2.0->pycyclops) (4.9.3)\r\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Requirement already satisfied: MarkupSafe>=2.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from Jinja2<4.0.0,>=3.1.3->pycyclops) (2.1.3)\r\n",
       "Requirement already satisfied: contourpy>=1.0.1 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from matplotlib<4.0.0,>=3.8.3->pycyclops) (1.1.0)\r\n",
       "Requirement already satisfied: cycler>=0.10 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from matplotlib<4.0.0,>=3.8.3->pycyclops) (0.12.1)\r\n",
       "Requirement already satisfied: fonttools>=4.22.0 in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from matplotlib<4.0.0,>=3.8.3->pycyclops) (4.47.0)\r\n",
@@ -136,7 +130,13 @@
      "output_type": "stream",
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-      "Requirement already satisfied: six in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pybtex<0.25.0,>=0.24.0->pycyclops) (1.16.0)\r\n",
+      "Requirement already satisfied: six in /mnt/data/actions_runners/cyclops-actions-runner-1/_work/cyclops/cyclops/.venv/lib/python3.10/site-packages (from pybtex<0.25.0,>=0.24.0->pycyclops) (1.16.0)\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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@@ -243,10 +243,10 @@
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-     "iopub.status.busy": "2024-06-04T04:07:45.796033Z",
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-     "shell.execute_reply": "2024-06-04T04:07:59.766936Z"
+     "iopub.execute_input": "2024-06-10T23:49:19.081004Z",
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@@ -312,10 +312,10 @@
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-     "iopub.status.busy": "2024-06-04T04:07:59.775984Z",
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-     "shell.execute_reply": "2024-06-04T04:07:59.782539Z"
+     "iopub.execute_input": "2024-06-10T23:49:32.741476Z",
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@@ -335,10 +335,10 @@
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    "metadata": {
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-     "iopub.status.busy": "2024-06-04T04:07:59.790061Z",
-     "iopub.status.idle": "2024-06-04T04:07:59.797456Z",
-     "shell.execute_reply": "2024-06-04T04:07:59.795938Z"
+     "iopub.execute_input": "2024-06-10T23:49:32.755602Z",
+     "iopub.status.busy": "2024-06-10T23:49:32.754480Z",
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@@ -362,10 +362,10 @@
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-     "iopub.status.busy": "2024-06-04T04:07:59.803331Z",
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-     "shell.execute_reply": "2024-06-04T04:08:10.173621Z"
+     "iopub.execute_input": "2024-06-10T23:49:32.769652Z",
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@@ -383,10 +383,10 @@
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-     "shell.execute_reply": "2024-06-04T04:08:10.202867Z"
+     "iopub.execute_input": "2024-06-10T23:49:37.775144Z",
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@@ -445,10 +445,10 @@
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-     "shell.execute_reply": "2024-06-04T04:08:10.423157Z"
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@@ -493,10 +493,10 @@
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@@ -516,10 +516,10 @@
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@@ -2490,10 +2490,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2024-06-04T04:08:11.488882Z",
-     "iopub.status.idle": "2024-06-04T04:08:11.505410Z",
-     "shell.execute_reply": "2024-06-04T04:08:11.503543Z"
+     "iopub.execute_input": "2024-06-10T23:49:38.913994Z",
+     "iopub.status.busy": "2024-06-10T23:49:38.912233Z",
+     "iopub.status.idle": "2024-06-10T23:49:38.929040Z",
+     "shell.execute_reply": "2024-06-10T23:49:38.927314Z"
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@@ -2517,10 +2517,10 @@
    "execution_count": 12,
    "metadata": {
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-     "iopub.status.idle": "2024-06-04T04:08:11.791038Z",
-     "shell.execute_reply": "2024-06-04T04:08:11.789186Z"
+     "iopub.execute_input": "2024-06-10T23:49:38.936282Z",
+     "iopub.status.busy": "2024-06-10T23:49:38.935208Z",
+     "iopub.status.idle": "2024-06-10T23:49:39.107895Z",
+     "shell.execute_reply": "2024-06-10T23:49:39.106057Z"
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     "tags": []
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@@ -4405,9 +4405,9 @@
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@@ -4487,10 +4487,10 @@
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@@ -4505,10 +4505,10 @@
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@@ -6370,9 +6370,9 @@
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@@ -6424,10 +6424,10 @@
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@@ -6444,10 +6444,10 @@
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@@ -6481,10 +6481,10 @@
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@@ -6509,10 +6509,10 @@
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    "metadata": {
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@@ -6521,7 +6521,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'number_diagnoses': 'ordinal', 'time_in_hospital': 'ordinal', 'medical_specialty': 'string', 'change': 'binary', 'discharge_disposition_id': 'ordinal', 'metformin': 'ordinal', 'num_procedures': 'ordinal', 'diag_2': 'string', 'diabetesMed': 'binary', 'diag_1': 'string', 'number_outpatient': 'ordinal', 'glipizide': 'ordinal', 'admission_type_id': 'ordinal', 'num_lab_procedures': 'numeric', 'num_medications': 'numeric', 'pioglitazone': 'ordinal', 'acarbose': 'binary', 'tolbutamide': 'binary', 'max_glu_serum': 'ordinal', 'rosiglitazone': 'ordinal', 'glimepiride': 'ordinal', 'number_emergency': 'ordinal', 'outcome': 'binary', 'A1Cresult': 'ordinal', 'repaglinide': 'ordinal', 'number_inpatient': 'ordinal', 'admission_source_id': 'ordinal', 'tolazamide': 'binary', 'gender': 'binary', 'diag_3': 'string', 'glyburide': 'ordinal', 'troglitazone': 'binary', 'age': 'ordinal', 'insulin': 'ordinal', 'race': 'ordinal'}\n"
+      "{'admission_type_id': 'ordinal', 'change': 'binary', 'time_in_hospital': 'ordinal', 'insulin': 'ordinal', 'metformin': 'ordinal', 'number_diagnoses': 'ordinal', 'diag_1': 'string', 'troglitazone': 'binary', 'diag_2': 'string', 'diag_3': 'string', 'glipizide': 'ordinal', 'tolazamide': 'binary', 'diabetesMed': 'binary', 'tolbutamide': 'binary', 'rosiglitazone': 'ordinal', 'A1Cresult': 'ordinal', 'glyburide': 'ordinal', 'medical_specialty': 'string', 'num_medications': 'numeric', 'num_lab_procedures': 'numeric', 'discharge_disposition_id': 'ordinal', 'age': 'ordinal', 'outcome': 'binary', 'gender': 'binary', 'max_glu_serum': 'ordinal', 'repaglinide': 'ordinal', 'number_emergency': 'ordinal', 'admission_source_id': 'ordinal', 'number_inpatient': 'ordinal', 'pioglitazone': 'ordinal', 'race': 'ordinal', 'glimepiride': 'ordinal', 'num_procedures': 'ordinal', 'number_outpatient': 'ordinal', 'acarbose': 'binary'}\n"
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@@ -6549,10 +6549,10 @@
    "execution_count": 20,
    "metadata": {
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-     "shell.execute_reply": "2024-06-04T04:08:12.263906Z"
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@@ -6572,10 +6572,10 @@
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-     "shell.execute_reply": "2024-06-04T04:08:12.288259Z"
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@@ -6601,10 +6601,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:08:12.297576Z",
-     "iopub.status.busy": "2024-06-04T04:08:12.296512Z",
-     "iopub.status.idle": "2024-06-04T04:08:12.398862Z",
-     "shell.execute_reply": "2024-06-04T04:08:12.397168Z"
+     "iopub.execute_input": "2024-06-10T23:49:39.661354Z",
+     "iopub.status.busy": "2024-06-10T23:49:39.660285Z",
+     "iopub.status.idle": "2024-06-10T23:49:39.765644Z",
+     "shell.execute_reply": "2024-06-10T23:49:39.763925Z"
     },
     "tags": []
    },
@@ -6638,10 +6638,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:08:12.406222Z",
-     "iopub.status.busy": "2024-06-04T04:08:12.404858Z",
-     "iopub.status.idle": "2024-06-04T04:08:12.413880Z",
-     "shell.execute_reply": "2024-06-04T04:08:12.412353Z"
+     "iopub.execute_input": "2024-06-10T23:49:39.773226Z",
+     "iopub.status.busy": "2024-06-10T23:49:39.771812Z",
+     "iopub.status.idle": "2024-06-10T23:49:39.781391Z",
+     "shell.execute_reply": "2024-06-10T23:49:39.779718Z"
     },
     "tags": []
    },
@@ -6681,10 +6681,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:08:12.420967Z",
-     "iopub.status.busy": "2024-06-04T04:08:12.419771Z",
-     "iopub.status.idle": "2024-06-04T04:08:12.696225Z",
-     "shell.execute_reply": "2024-06-04T04:08:12.694443Z"
+     "iopub.execute_input": "2024-06-10T23:49:39.788449Z",
+     "iopub.status.busy": "2024-06-10T23:49:39.787431Z",
+     "iopub.status.idle": "2024-06-10T23:49:40.129201Z",
+     "shell.execute_reply": "2024-06-10T23:49:40.127302Z"
     }
    },
    "outputs": [],
@@ -6728,10 +6728,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:08:12.704442Z",
-     "iopub.status.busy": "2024-06-04T04:08:12.703445Z",
-     "iopub.status.idle": "2024-06-04T04:08:12.928480Z",
-     "shell.execute_reply": "2024-06-04T04:08:12.926832Z"
+     "iopub.execute_input": "2024-06-10T23:49:40.137325Z",
+     "iopub.status.busy": "2024-06-10T23:49:40.135843Z",
+     "iopub.status.idle": "2024-06-10T23:49:40.465128Z",
+     "shell.execute_reply": "2024-06-10T23:49:40.463151Z"
     },
     "tags": []
    },
@@ -6758,10 +6758,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:08:12.936127Z",
-     "iopub.status.busy": "2024-06-04T04:08:12.934628Z",
-     "iopub.status.idle": "2024-06-04T04:08:13.095448Z",
-     "shell.execute_reply": "2024-06-04T04:08:13.093537Z"
+     "iopub.execute_input": "2024-06-10T23:49:40.473137Z",
+     "iopub.status.busy": "2024-06-10T23:49:40.471608Z",
+     "iopub.status.idle": "2024-06-10T23:49:40.618344Z",
+     "shell.execute_reply": "2024-06-10T23:49:40.616517Z"
     },
     "tags": []
    },
@@ -6769,7 +6769,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f37fdf98ff1d45df82b92f2c28c7644b",
+       "model_id": "af6acbeccb844708bd464fa92e11fd7f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -6804,10 +6804,10 @@
    "execution_count": 27,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:08:13.103730Z",
-     "iopub.status.busy": "2024-06-04T04:08:13.102635Z",
-     "iopub.status.idle": "2024-06-04T04:08:16.008853Z",
-     "shell.execute_reply": "2024-06-04T04:08:16.006931Z"
+     "iopub.execute_input": "2024-06-10T23:49:40.626084Z",
+     "iopub.status.busy": "2024-06-10T23:49:40.624710Z",
+     "iopub.status.idle": "2024-06-10T23:49:43.841269Z",
+     "shell.execute_reply": "2024-06-10T23:49:43.839359Z"
     },
     "tags": []
    },
@@ -6831,10 +6831,10 @@
    "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:08:16.016771Z",
-     "iopub.status.busy": "2024-06-04T04:08:16.015860Z",
-     "iopub.status.idle": "2024-06-04T04:08:16.024486Z",
-     "shell.execute_reply": "2024-06-04T04:08:16.022778Z"
+     "iopub.execute_input": "2024-06-10T23:49:43.852847Z",
+     "iopub.status.busy": "2024-06-10T23:49:43.851283Z",
+     "iopub.status.idle": "2024-06-10T23:49:43.859982Z",
+     "shell.execute_reply": "2024-06-10T23:49:43.858287Z"
     },
     "tags": []
    },
@@ -6852,10 +6852,10 @@
    "execution_count": 29,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:08:16.031990Z",
-     "iopub.status.busy": "2024-06-04T04:08:16.030568Z",
-     "iopub.status.idle": "2024-06-04T04:08:16.041356Z",
-     "shell.execute_reply": "2024-06-04T04:08:16.039704Z"
+     "iopub.execute_input": "2024-06-10T23:49:43.866944Z",
+     "iopub.status.busy": "2024-06-10T23:49:43.865889Z",
+     "iopub.status.idle": "2024-06-10T23:49:43.876821Z",
+     "shell.execute_reply": "2024-06-10T23:49:43.875157Z"
     },
     "tags": []
    },
@@ -6891,10 +6891,10 @@
    "execution_count": 30,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:08:16.048449Z",
-     "iopub.status.busy": "2024-06-04T04:08:16.047460Z",
-     "iopub.status.idle": "2024-06-04T04:14:27.827471Z",
-     "shell.execute_reply": "2024-06-04T04:14:27.825373Z"
+     "iopub.execute_input": "2024-06-10T23:49:43.883854Z",
+     "iopub.status.busy": "2024-06-10T23:49:43.882836Z",
+     "iopub.status.idle": "2024-06-10T23:50:08.532211Z",
+     "shell.execute_reply": "2024-06-10T23:50:08.530367Z"
     },
     "tags": []
    },
@@ -6903,49 +6903,49 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:14:27,608 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best scale_pos_weight: 9\n"
+      "2024-06-10 19:50:08,481 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best scale_pos_weight: 9\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:14:27,713 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 1\n"
+      "2024-06-10 19:50:08,486 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 1\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:14:27,714 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 500\n"
+      "2024-06-10 19:50:08,487 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 250\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:14:27,717 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
+      "2024-06-10 19:50:08,489 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:14:27,719 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
+      "2024-06-10 19:50:08,490 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:14:27,721 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 2\n"
+      "2024-06-10 19:50:08,491 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:14:27,723 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.8\n"
+      "2024-06-10 19:50:08,493 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.8\n"
      ]
     },
     {
@@ -7359,22 +7359,22 @@
        "              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=2, gpu_id=None, grow_policy=None,\n",
+       "              feature_types=None, gamma=0, 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",
+       "              monotone_constraints=None, n_estimators=250, 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=2, gpu_id=None, grow_policy=None,\n",
+       "              feature_types=None, gamma=0, 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",
+       "              monotone_constraints=None, n_estimators=250, n_jobs=None,\n",
        "              num_parallel_tree=None, predictor=None, random_state=123, ...)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
@@ -7382,12 +7382,12 @@
        "              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=2, gpu_id=None, grow_policy=None,\n",
+       "              feature_types=None, gamma=0, 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",
+       "              monotone_constraints=None, n_estimators=250, n_jobs=None,\n",
        "              num_parallel_tree=None, predictor=None, random_state=123, ...)"
       ]
      },
@@ -7420,10 +7420,10 @@
    "execution_count": 31,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:14:27.895225Z",
-     "iopub.status.busy": "2024-06-04T04:14:27.890723Z",
-     "iopub.status.idle": "2024-06-04T04:14:27.907353Z",
-     "shell.execute_reply": "2024-06-04T04:14:27.905642Z"
+     "iopub.execute_input": "2024-06-10T23:50:08.540403Z",
+     "iopub.status.busy": "2024-06-10T23:50:08.538829Z",
+     "iopub.status.idle": "2024-06-10T23:50:08.550985Z",
+     "shell.execute_reply": "2024-06-10T23:50:08.549208Z"
     }
    },
    "outputs": [
@@ -7431,7 +7431,7 @@
      "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': 0.8, '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': 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': 0.8, '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': 250, '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"
      ]
     }
    ],
@@ -7454,10 +7454,10 @@
    "execution_count": 32,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:14:27.914842Z",
-     "iopub.status.busy": "2024-06-04T04:14:27.913127Z",
-     "iopub.status.idle": "2024-06-04T04:14:27.957240Z",
-     "shell.execute_reply": "2024-06-04T04:14:27.955448Z"
+     "iopub.execute_input": "2024-06-10T23:50:08.558804Z",
+     "iopub.status.busy": "2024-06-10T23:50:08.557708Z",
+     "iopub.status.idle": "2024-06-10T23:50:08.571224Z",
+     "shell.execute_reply": "2024-06-10T23:50:08.569447Z"
     }
    },
    "outputs": [],
@@ -7479,10 +7479,10 @@
    "execution_count": 33,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:14:27.966009Z",
-     "iopub.status.busy": "2024-06-04T04:14:27.964308Z",
-     "iopub.status.idle": "2024-06-04T04:14:28.717152Z",
-     "shell.execute_reply": "2024-06-04T04:14:28.715485Z"
+     "iopub.execute_input": "2024-06-10T23:50:08.578191Z",
+     "iopub.status.busy": "2024-06-10T23:50:08.577106Z",
+     "iopub.status.idle": "2024-06-10T23:50:08.954218Z",
+     "shell.execute_reply": "2024-06-10T23:50:08.952269Z"
     },
     "tags": []
    },
@@ -7490,7 +7490,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dabac22fd0ef42449c5caddf50c2d20c",
+       "model_id": "83ab6e0792884c94a35eb1c3609dc641",
        "version_major": 2,
        "version_minor": 0
       },
@@ -7535,10 +7535,10 @@
    "execution_count": 34,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:14:28.724537Z",
-     "iopub.status.busy": "2024-06-04T04:14:28.723686Z",
-     "iopub.status.idle": "2024-06-04T04:14:28.758023Z",
-     "shell.execute_reply": "2024-06-04T04:14:28.756348Z"
+     "iopub.execute_input": "2024-06-10T23:50:08.962096Z",
+     "iopub.status.busy": "2024-06-10T23:50:08.960730Z",
+     "iopub.status.idle": "2024-06-10T23:50:08.998921Z",
+     "shell.execute_reply": "2024-06-10T23:50:08.997065Z"
     },
     "tags": []
    },
@@ -7572,10 +7572,10 @@
    "execution_count": 35,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:14:28.765295Z",
-     "iopub.status.busy": "2024-06-04T04:14:28.764166Z",
-     "iopub.status.idle": "2024-06-04T04:14:28.773388Z",
-     "shell.execute_reply": "2024-06-04T04:14:28.771833Z"
+     "iopub.execute_input": "2024-06-10T23:50:09.006635Z",
+     "iopub.status.busy": "2024-06-10T23:50:09.005138Z",
+     "iopub.status.idle": "2024-06-10T23:50:09.014669Z",
+     "shell.execute_reply": "2024-06-10T23:50:09.012908Z"
     },
     "tags": []
    },
@@ -7608,10 +7608,10 @@
    "execution_count": 36,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:14:28.781002Z",
-     "iopub.status.busy": "2024-06-04T04:14:28.779337Z",
-     "iopub.status.idle": "2024-06-04T04:14:28.797559Z",
-     "shell.execute_reply": "2024-06-04T04:14:28.796005Z"
+     "iopub.execute_input": "2024-06-10T23:50:09.021751Z",
+     "iopub.status.busy": "2024-06-10T23:50:09.020691Z",
+     "iopub.status.idle": "2024-06-10T23:50:09.039934Z",
+     "shell.execute_reply": "2024-06-10T23:50:09.038177Z"
     },
     "tags": []
    },
@@ -7646,10 +7646,10 @@
    "execution_count": 37,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:14:28.805522Z",
-     "iopub.status.busy": "2024-06-04T04:14:28.803764Z",
-     "iopub.status.idle": "2024-06-04T04:14:28.812882Z",
-     "shell.execute_reply": "2024-06-04T04:14:28.811350Z"
+     "iopub.execute_input": "2024-06-10T23:50:09.047101Z",
+     "iopub.status.busy": "2024-06-10T23:50:09.046042Z",
+     "iopub.status.idle": "2024-06-10T23:50:09.055224Z",
+     "shell.execute_reply": "2024-06-10T23:50:09.053476Z"
     },
     "tags": []
    },
@@ -7677,10 +7677,10 @@
    "execution_count": 38,
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    "execution_count": 46,
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-     "shell.execute_reply": "2024-06-04T04:14:32.237233Z"
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+     "shell.execute_reply": "2024-06-10T23:50:12.008494Z"
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    "outputs": [],
@@ -11245,10 +11245,10 @@
    "execution_count": 47,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:14:32.245496Z",
-     "iopub.status.busy": "2024-06-04T04:14:32.244538Z",
-     "iopub.status.idle": "2024-06-04T04:14:32.515511Z",
-     "shell.execute_reply": "2024-06-04T04:14:32.513908Z"
+     "iopub.execute_input": "2024-06-10T23:50:12.017517Z",
+     "iopub.status.busy": "2024-06-10T23:50:12.016099Z",
+     "iopub.status.idle": "2024-06-10T23:50:12.077166Z",
+     "shell.execute_reply": "2024-06-10T23:50:12.075464Z"
     }
    },
    "outputs": [
@@ -11270,12 +11270,12 @@
           "BinaryAveragePrecision"
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-          0.1428571492433548,
-          0.10526315867900848,
-          0.12121212482452393,
-          0.6420471668243408,
-          0.15128837525844574
+          0.8700000047683716,
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+          0.15789473056793213,
+          0.1875,
+          0.6667636036872864,
+          0.18046614527702332
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@@ -12112,9 +12112,9 @@
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@@ -12162,10 +12162,10 @@
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    "metadata": {
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-     "iopub.status.idle": "2024-06-04T04:14:32.532083Z",
-     "shell.execute_reply": "2024-06-04T04:14:32.530491Z"
+     "iopub.execute_input": "2024-06-10T23:50:12.084894Z",
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@@ -12186,10 +12186,10 @@
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@@ -17267,10 +17269,10 @@
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@@ -17289,10 +17291,10 @@
    "execution_count": 53,
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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 39e250506..de1297813 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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-     "shell.execute_reply": "2024-06-04T04:15:46.709472Z"
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@@ -90,10 +90,10 @@
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@@ -113,10 +113,10 @@
    "execution_count": 3,
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@@ -142,10 +142,10 @@
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@@ -165,10 +165,10 @@
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-     "shell.execute_reply": "2024-06-04T04:15:47.264719Z"
+     "iopub.execute_input": "2024-06-10T23:50:40.042165Z",
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@@ -177,7 +177,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:15:47,136 \u001b[1;37mINFO\u001b[0m cyclops.utils.file - Loading DataFrame from ./data/heart.csv\n"
+      "2024-06-10 19:50:40,045 \u001b[1;37mINFO\u001b[0m cyclops.utils.file - Loading DataFrame from ./data/heart.csv\n"
      ]
     },
     {
@@ -233,10 +233,10 @@
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@@ -2044,9 +2044,9 @@
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@@ -3985,10 +3985,10 @@
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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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-     "shell.execute_reply": "2024-06-04T04:15:52.734748Z"
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@@ -5887,10 +5887,10 @@
    "execution_count": 13,
    "metadata": {
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-     "shell.execute_reply": "2024-06-04T04:15:52.752161Z"
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@@ -5917,10 +5917,10 @@
    "execution_count": 14,
    "metadata": {
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-     "shell.execute_reply": "2024-06-04T04:15:52.767098Z"
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@@ -5952,10 +5952,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:15:52.775035Z",
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-     "shell.execute_reply": "2024-06-04T04:15:52.781574Z"
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@@ -5992,10 +5992,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:15:52.789771Z",
-     "iopub.status.busy": "2024-06-04T04:15:52.788752Z",
-     "iopub.status.idle": "2024-06-04T04:15:52.854685Z",
-     "shell.execute_reply": "2024-06-04T04:15:52.852994Z"
+     "iopub.execute_input": "2024-06-10T23:50:41.360422Z",
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     },
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@@ -6004,7 +6004,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'Oldpeak': 'numeric', 'RestingECG': 'ordinal', 'Cholesterol': 'numeric', 'ST_Slope': 'ordinal', 'MaxHR': 'numeric', 'outcome': 'binary', 'Age': 'numeric', 'ChestPainType': 'ordinal', 'RestingBP': 'numeric', 'ExerciseAngina': 'binary', 'FastingBS': 'binary', 'Sex': 'binary'}\n"
+      "{'FastingBS': 'binary', 'outcome': 'binary', 'Sex': 'binary', 'ST_Slope': 'ordinal', 'RestingECG': 'ordinal', 'Cholesterol': 'numeric', 'Oldpeak': 'numeric', 'RestingBP': 'numeric', 'ChestPainType': 'ordinal', 'ExerciseAngina': 'binary', 'Age': 'numeric', 'MaxHR': 'numeric'}\n"
      ]
     }
    ],
@@ -6032,10 +6032,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:15:52.861921Z",
-     "iopub.status.busy": "2024-06-04T04:15:52.860770Z",
-     "iopub.status.idle": "2024-06-04T04:15:52.870197Z",
-     "shell.execute_reply": "2024-06-04T04:15:52.868599Z"
+     "iopub.execute_input": "2024-06-10T23:50:41.435724Z",
+     "iopub.status.busy": "2024-06-10T23:50:41.434237Z",
+     "iopub.status.idle": "2024-06-10T23:50:41.443839Z",
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@@ -6055,10 +6055,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:15:52.877119Z",
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-     "shell.execute_reply": "2024-06-04T04:15:52.898932Z"
+     "iopub.execute_input": "2024-06-10T23:50:41.450290Z",
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+     "shell.execute_reply": "2024-06-10T23:50:41.472345Z"
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@@ -6084,10 +6084,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:15:52.907471Z",
-     "iopub.status.busy": "2024-06-04T04:15:52.906421Z",
-     "iopub.status.idle": "2024-06-04T04:15:52.934866Z",
-     "shell.execute_reply": "2024-06-04T04:15:52.933184Z"
+     "iopub.execute_input": "2024-06-10T23:50:41.481526Z",
+     "iopub.status.busy": "2024-06-10T23:50:41.480478Z",
+     "iopub.status.idle": "2024-06-10T23:50:41.507668Z",
+     "shell.execute_reply": "2024-06-10T23:50:41.506025Z"
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@@ -6118,10 +6118,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
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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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      "text": [
-      "2024-06-04 00:16:14,501 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best alpha: 0.001\n"
+      "2024-06-10 19:51:02,352 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best alpha: 0.001\n"
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-      "2024-06-04 00:16:14,503 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best eta0: 0.01\n"
+      "2024-06-10 19:51:02,355 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best eta0: 0.01\n"
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-      "2024-06-04 00:16:14,504 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: adaptive\n"
+      "2024-06-10 19:51:02,356 \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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@@ -7045,10 +7045,10 @@
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@@ -7083,10 +7083,10 @@
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@@ -7115,10 +7115,10 @@
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@@ -7126,7 +7126,7 @@
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@@ -7140,7 +7140,7 @@
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@@ -7154,7 +7154,7 @@
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@@ -7168,7 +7168,7 @@
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@@ -7182,7 +7182,7 @@
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@@ -7196,7 +7196,7 @@
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@@ -7210,7 +7210,7 @@
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@@ -7224,7 +7224,7 @@
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@@ -7238,7 +7238,7 @@
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@@ -7252,7 +7252,7 @@
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@@ -7283,17 +7283,17 @@
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@@ -7307,7 +7307,7 @@
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@@ -7321,7 +7321,7 @@
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@@ -7335,7 +7335,7 @@
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@@ -7349,7 +7349,7 @@
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@@ -7396,10 +7396,10 @@
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@@ -7468,10 +7468,10 @@
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    "outputs": [],
@@ -7485,10 +7485,10 @@
    "execution_count": 39,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:16:16.484140Z",
-     "iopub.status.busy": "2024-06-04T04:16:16.483279Z",
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    },
    "outputs": [
@@ -7521,10 +7521,10 @@
    "execution_count": 40,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:16:16.502171Z",
-     "iopub.status.busy": "2024-06-04T04:16:16.501200Z",
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-     "shell.execute_reply": "2024-06-04T04:16:16.592905Z"
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    "outputs": [
@@ -9132,9 +9132,9 @@
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        "                            \n",
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@@ -9179,10 +9179,10 @@
    "execution_count": 41,
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2024-06-04T04:16:16.604264Z",
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-     "shell.execute_reply": "2024-06-04T04:16:16.616820Z"
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    "outputs": [
@@ -9215,10 +9215,10 @@
    "execution_count": 42,
    "metadata": {
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-     "iopub.status.idle": "2024-06-04T04:16:16.689567Z",
-     "shell.execute_reply": "2024-06-04T04:16:16.687961Z"
+     "iopub.execute_input": "2024-06-10T23:51:04.272183Z",
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@@ -10810,9 +10810,9 @@
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    "outputs": [],
@@ -10881,10 +10881,10 @@
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-     "shell.execute_reply": "2024-06-04T04:16:16.771091Z"
+     "iopub.execute_input": "2024-06-10T23:51:04.361414Z",
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+     "shell.execute_reply": "2024-06-10T23:51:04.415993Z"
     }
    },
    "outputs": [
@@ -11748,9 +11748,9 @@
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@@ -11822,10 +11822,10 @@
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@@ -12779,10 +12779,10 @@
    "execution_count": 47,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:16:16.865372Z",
-     "iopub.status.busy": "2024-06-04T04:16:16.863976Z",
-     "iopub.status.idle": "2024-06-04T04:16:17.476194Z",
-     "shell.execute_reply": "2024-06-04T04:16:17.474622Z"
+     "iopub.execute_input": "2024-06-10T23:51:04.511111Z",
+     "iopub.status.busy": "2024-06-10T23:51:04.510249Z",
+     "iopub.status.idle": "2024-06-10T23:51:05.079628Z",
+     "shell.execute_reply": "2024-06-10T23:51:05.077803Z"
     }
    },
    "outputs": [
@@ -15481,9 +15481,9 @@
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@@ -16749,10 +16749,10 @@
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@@ -16771,10 +16771,10 @@
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index ac8e11d44..801b876f1 100644
--- a/api/tutorials/kaggle/heart_failure_report_periodic.html
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@@ -1264,23 +1264,23 @@ <h3>Sensitive Data</h3>
   
     
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-          
-          <br>
-        
+        @misc{fedesoriano,
+  title={Heart Failure Prediction Dataset.},
+  author={Fedesoriano, F},
+  year={2021},
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-        @misc{fedesoriano,
-  title={Heart Failure Prediction Dataset.},
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@@ -1798,7 +1798,7 @@ <h3>Version</h3>
         
           
             
-              <b>Date:</b> 2024-06-04
+              <b>Date:</b> 2024-06-10
             
           
           <br>
@@ -2017,8 +2017,8 @@ <h2 class="collapsible">Model Parameters</h2>
   
     
     <div class="subcard">
-      <h3>Average</h3>
-      False
+      <h3>Shuffle</h3>
+      True
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@@ -2026,8 +2026,8 @@ <h3>Average</h3>
   
     
     <div class="subcard">
-      <h3>Early_stopping</h3>
-      True
+      <h3>Power_t</h3>
+      0.5
     </div>
     
   
@@ -2035,8 +2035,8 @@ <h3>Early_stopping</h3>
   
     
     <div class="subcard">
-      <h3>Penalty</h3>
-      l2
+      <h3>Average</h3>
+      False
     </div>
     
   
@@ -2044,8 +2044,8 @@ <h3>Penalty</h3>
   
     
     <div class="subcard">
-      <h3>Fit_intercept</h3>
-      True
+      <h3>Tol</h3>
+      0.001
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@@ -2053,8 +2053,8 @@ <h3>Fit_intercept</h3>
   
     
     <div class="subcard">
-      <h3>Max_iter</h3>
-      1000
+      <h3>Verbose</h3>
+      0
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@@ -2062,8 +2062,8 @@ <h3>Max_iter</h3>
   
     
     <div class="subcard">
-      <h3>Class_weight</h3>
-      balanced
+      <h3>Fit_intercept</h3>
+      True
     </div>
     
   
@@ -2071,8 +2071,8 @@ <h3>Class_weight</h3>
   
     
     <div class="subcard">
-      <h3>Power_t</h3>
-      0.5
+      <h3>Alpha</h3>
+      0.001
     </div>
     
   
@@ -2080,8 +2080,8 @@ <h3>Power_t</h3>
   
     
     <div class="subcard">
-      <h3>L1_ratio</h3>
-      0.15
+      <h3>Early_stopping</h3>
+      True
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@@ -2089,8 +2089,8 @@ <h3>L1_ratio</h3>
   
     
     <div class="subcard">
-      <h3>Eta0</h3>
-      0.01
+      <h3>Learning_rate</h3>
+      adaptive
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@@ -2098,8 +2098,8 @@ <h3>Eta0</h3>
   
     
     <div class="subcard">
-      <h3>Loss</h3>
-      log_loss
+      <h3>Validation_fraction</h3>
+      0.1
     </div>
     
   
@@ -2107,8 +2107,8 @@ <h3>Loss</h3>
   
     
     <div class="subcard">
-      <h3>Shuffle</h3>
-      True
+      <h3>Epsilon</h3>
+      0.1
     </div>
     
   
@@ -2116,8 +2116,8 @@ <h3>Shuffle</h3>
   
     
     <div class="subcard">
-      <h3>Validation_fraction</h3>
-      0.1
+      <h3>Loss</h3>
+      log_loss
     </div>
     
   
@@ -2125,8 +2125,8 @@ <h3>Validation_fraction</h3>
   
     
     <div class="subcard">
-      <h3>Verbose</h3>
-      0
+      <h3>Max_iter</h3>
+      1000
     </div>
     
   
@@ -2134,8 +2134,8 @@ <h3>Verbose</h3>
   
     
     <div class="subcard">
-      <h3>Warm_start</h3>
-      False
+      <h3>Eta0</h3>
+      0.01
     </div>
     
   
@@ -2143,8 +2143,8 @@ <h3>Warm_start</h3>
   
     
     <div class="subcard">
-      <h3>Alpha</h3>
-      0.001
+      <h3>Random_state</h3>
+      123
     </div>
     
   
@@ -2152,8 +2152,8 @@ <h3>Alpha</h3>
   
     
     <div class="subcard">
-      <h3>N_iter_no_change</h3>
-      5
+      <h3>L1_ratio</h3>
+      0.15
     </div>
     
   
@@ -2161,8 +2161,8 @@ <h3>N_iter_no_change</h3>
   
     
     <div class="subcard">
-      <h3>Tol</h3>
-      0.001
+      <h3>Penalty</h3>
+      l2
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@@ -2175,8 +2175,8 @@ <h3>Tol</h3>
   
     
     <div class="subcard">
-      <h3>Random_state</h3>
-      123
+      <h3>Warm_start</h3>
+      False
     </div>
     
   
@@ -2184,8 +2184,8 @@ <h3>Random_state</h3>
   
     
     <div class="subcard">
-      <h3>Learning_rate</h3>
-      adaptive
+      <h3>Class_weight</h3>
+      balanced
     </div>
     
   
@@ -2193,8 +2193,8 @@ <h3>Learning_rate</h3>
   
     
     <div class="subcard">
-      <h3>Epsilon</h3>
-      0.1
+      <h3>N_iter_no_change</h3>
+      5
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diff --git a/api/tutorials/mimiciv/mortality_prediction.html b/api/tutorials/mimiciv/mortality_prediction.html
index 7abc50187..8ceef95fc 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>
-2024-06-04 00:16:54,195 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Database setup, ready to run queries!
+2024-06-10 19:51:39,324 <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>
-2024-06-04 00:17:13,129 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-10 19:51:58,899 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -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>
-2024-06-04 00:17:13,131 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 16.708294 s
+2024-06-10 19:51:58,901 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 17.239892 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>
-2024-06-04 00:19:35,600 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-10 19:53:37,026 <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>
-2024-06-04 00:19:35,604 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 139.243567 s
+2024-06-10 19:53:37,028 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 95.157216 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">
 </div>
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@@ -1003,7 +1003,7 @@ <h3>Identifying feature types<a class="headerlink" href="#Identifying-feature-ty
 </div>
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-{&#39;lab_ptt&#39;: &#39;numeric&#39;, &#39;mortality_outcome&#39;: &#39;binary&#39;, &#39;lab_bicarbonate&#39;: &#39;numeric&#39;, &#39;lab_white blood cells&#39;: &#39;numeric&#39;, &#39;lab_hematocrit&#39;: &#39;numeric&#39;, &#39;lab_potassium&#39;: &#39;numeric&#39;, &#39;lab_asparate aminotransferase (ast)&#39;: &#39;numeric&#39;, &#39;lab_mcv&#39;: &#39;numeric&#39;, &#39;lab_pt&#39;: &#39;numeric&#39;, &#39;lab_i&#39;: &#39;numeric&#39;, &#39;lab_ph&#39;: &#39;numeric&#39;, &#39;lab_inr(pt)&#39;: &#39;numeric&#39;, &#39;lab_mch&#39;: &#39;numeric&#39;, &#39;lab_chloride&#39;: &#39;numeric&#39;, &#39;lab_platelet count&#39;: &#39;numeric&#39;, &#39;lab_glucose&#39;: &#39;numeric&#39;, &#39;lab_creatinine&#39;: &#39;numeric&#39;, &#39;lab_rdw&#39;: &#39;numeric&#39;, &#39;lab_red blood cells&#39;: &#39;numeric&#39;, &#39;lab_h&#39;: &#39;numeric&#39;, &#39;lab_phosphate&#39;: &#39;numeric&#39;, &#39;lab_urea nitrogen&#39;: &#39;numeric&#39;, &#39;age&#39;: &#39;numeric&#39;, &#39;lab_hemoglobin&#39;: &#39;numeric&#39;, &#39;lab_l&#39;: &#39;numeric&#39;, &#39;lab_rdw-sd&#39;: &#39;numeric&#39;, &#39;lab_alkaline phosphatase&#39;: &#39;numeric&#39;, &#39;gender&#39;: &#39;binary&#39;, &#39;lab_magnesium&#39;: &#39;numeric&#39;, &#39;lab_bilirubin, total&#39;: &#39;numeric&#39;, &#39;lab_calcium, total&#39;: &#39;numeric&#39;, &#39;lab_mchc&#39;: &#39;numeric&#39;, &#39;admission_location&#39;: &#39;ordinal&#39;, &#39;lab_anion gap&#39;: &#39;numeric&#39;, &#39;lab_alanine aminotransferase (alt)&#39;: &#39;numeric&#39;, &#39;lab_sodium&#39;: &#39;numeric&#39;}
+{&#39;lab_mch&#39;: &#39;numeric&#39;, &#39;gender&#39;: &#39;binary&#39;, &#39;lab_bilirubin, total&#39;: &#39;numeric&#39;, &#39;age&#39;: &#39;numeric&#39;, &#39;lab_rdw-sd&#39;: &#39;numeric&#39;, &#39;lab_calcium, total&#39;: &#39;numeric&#39;, &#39;lab_l&#39;: &#39;numeric&#39;, &#39;lab_pt&#39;: &#39;numeric&#39;, &#39;lab_glucose&#39;: &#39;numeric&#39;, &#39;lab_platelet count&#39;: &#39;numeric&#39;, &#39;mortality_outcome&#39;: &#39;binary&#39;, &#39;lab_chloride&#39;: &#39;numeric&#39;, &#39;lab_hematocrit&#39;: &#39;numeric&#39;, &#39;lab_asparate aminotransferase (ast)&#39;: &#39;numeric&#39;, &#39;lab_mcv&#39;: &#39;numeric&#39;, &#39;admission_location&#39;: &#39;ordinal&#39;, &#39;lab_mchc&#39;: &#39;numeric&#39;, &#39;lab_inr(pt)&#39;: &#39;numeric&#39;, &#39;lab_white blood cells&#39;: &#39;numeric&#39;, &#39;lab_urea nitrogen&#39;: &#39;numeric&#39;, &#39;lab_bicarbonate&#39;: &#39;numeric&#39;, &#39;lab_anion gap&#39;: &#39;numeric&#39;, &#39;lab_i&#39;: &#39;numeric&#39;, &#39;lab_phosphate&#39;: &#39;numeric&#39;, &#39;lab_hemoglobin&#39;: &#39;numeric&#39;, &#39;lab_ptt&#39;: &#39;numeric&#39;, &#39;lab_creatinine&#39;: &#39;numeric&#39;, &#39;lab_magnesium&#39;: &#39;numeric&#39;, &#39;lab_ph&#39;: &#39;numeric&#39;, &#39;lab_rdw&#39;: &#39;numeric&#39;, &#39;lab_alanine aminotransferase (alt)&#39;: &#39;numeric&#39;, &#39;lab_h&#39;: &#39;numeric&#39;, &#39;lab_red blood cells&#39;: &#39;numeric&#39;, &#39;lab_sodium&#39;: &#39;numeric&#39;, &#39;lab_alkaline phosphatase&#39;: &#39;numeric&#39;, &#39;lab_potassium&#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": "acc4e877968349a387a19fb2f0883df5"}</script></div>
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@@ -1205,7 +1205,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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 <div class="output_area stderr docutils container">
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-2024-06-04 00:26:45,562 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best reg_lambda: 0
+2024-06-10 19:58:36,797 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best reg_lambda: 1
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@@ -1213,7 +1213,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-04 00:26:45,583 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best n_estimators: 250
+2024-06-10 19:58:36,812 <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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-2024-06-04 00:26:45,584 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best max_depth: 5
+2024-06-10 19:58:36,814 <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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-2024-06-04 00:26:45,586 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2024-06-10 19:58:36,816 <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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-2024-06-04 00:26:45,587 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 1
+2024-06-10 19:58:36,817 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 0
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@@ -1245,7 +1245,7 @@ <h2>Training<a class="headerlink" href="#Training" title="Link to this heading">
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-2024-06-04 00:26:45,588 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
+2024-06-10 19:58:36,818 <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=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, ...)</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=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, ...)</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;: 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}
+{&#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;: 0, &#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;: 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}
 </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>
 <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/mimiciv/mortality_prediction.ipynb b/api/tutorials/mimiciv/mortality_prediction.ipynb
index 79682f116..94cea2782 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": "2024-06-04T04:16:51.606937Z"
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@@ -89,10 +89,10 @@
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-     "shell.execute_reply": "2024-06-04T04:16:51.622204Z"
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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": [
-      "2024-06-04 00:16:54,195 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Database setup, ready to run queries!\n"
+      "2024-06-10 19:51:39,324 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Database setup, ready to run queries!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:17:13,129 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
+      "2024-06-10 19:51:58,899 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:17:13,131 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 16.708294 s\n"
+      "2024-06-10 19:51:58,901 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 17.239892 s\n"
      ]
     },
     {
@@ -196,14 +196,14 @@
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      "output_type": "stream",
      "text": [
-      "2024-06-04 00:19:35,600 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
+      "2024-06-10 19:53:37,026 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
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      "output_type": "stream",
      "text": [
-      "2024-06-04 00:19:35,604 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 139.243567 s\n"
+      "2024-06-10 19:53:37,028 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 95.157216 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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-     "shell.execute_reply": "2024-06-04T04:23:53.915108Z"
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@@ -48181,10 +48181,10 @@
    "execution_count": 15,
    "metadata": {
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@@ -48192,7 +48192,7 @@
      "name": "stdout",
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      "text": [
-      "{'lab_ptt': 'numeric', 'mortality_outcome': 'binary', 'lab_bicarbonate': 'numeric', 'lab_white blood cells': 'numeric', 'lab_hematocrit': 'numeric', 'lab_potassium': 'numeric', 'lab_asparate aminotransferase (ast)': 'numeric', 'lab_mcv': 'numeric', 'lab_pt': 'numeric', 'lab_i': 'numeric', 'lab_ph': 'numeric', 'lab_inr(pt)': 'numeric', 'lab_mch': 'numeric', 'lab_chloride': 'numeric', 'lab_platelet count': 'numeric', 'lab_glucose': 'numeric', 'lab_creatinine': 'numeric', 'lab_rdw': 'numeric', 'lab_red blood cells': 'numeric', 'lab_h': 'numeric', 'lab_phosphate': 'numeric', 'lab_urea nitrogen': 'numeric', 'age': 'numeric', 'lab_hemoglobin': 'numeric', 'lab_l': 'numeric', 'lab_rdw-sd': 'numeric', 'lab_alkaline phosphatase': 'numeric', 'gender': 'binary', 'lab_magnesium': 'numeric', 'lab_bilirubin, total': 'numeric', 'lab_calcium, total': 'numeric', 'lab_mchc': 'numeric', 'admission_location': 'ordinal', 'lab_anion gap': 'numeric', 'lab_alanine aminotransferase (alt)': 'numeric', 'lab_sodium': 'numeric'}\n"
+      "{'lab_mch': 'numeric', 'gender': 'binary', 'lab_bilirubin, total': 'numeric', 'age': 'numeric', 'lab_rdw-sd': 'numeric', 'lab_calcium, total': 'numeric', 'lab_l': 'numeric', 'lab_pt': 'numeric', 'lab_glucose': 'numeric', 'lab_platelet count': 'numeric', 'mortality_outcome': 'binary', 'lab_chloride': 'numeric', 'lab_hematocrit': 'numeric', 'lab_asparate aminotransferase (ast)': 'numeric', 'lab_mcv': 'numeric', 'admission_location': 'ordinal', 'lab_mchc': 'numeric', 'lab_inr(pt)': 'numeric', 'lab_white blood cells': 'numeric', 'lab_urea nitrogen': 'numeric', 'lab_bicarbonate': 'numeric', 'lab_anion gap': 'numeric', 'lab_i': 'numeric', 'lab_phosphate': 'numeric', 'lab_hemoglobin': 'numeric', 'lab_ptt': 'numeric', 'lab_creatinine': 'numeric', 'lab_magnesium': 'numeric', 'lab_ph': 'numeric', 'lab_rdw': 'numeric', 'lab_alanine aminotransferase (alt)': 'numeric', 'lab_h': 'numeric', 'lab_red blood cells': 'numeric', 'lab_sodium': 'numeric', 'lab_alkaline phosphatase': 'numeric', 'lab_potassium': 'numeric'}\n"
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@@ -48222,10 +48222,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2024-06-04T04:23:54.321354Z"
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@@ -48243,10 +48243,10 @@
    "execution_count": 17,
    "metadata": {
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-     "shell.execute_reply": "2024-06-04T04:23:54.428918Z"
+     "iopub.execute_input": "2024-06-10T23:56:16.815079Z",
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@@ -48271,10 +48271,10 @@
    "execution_count": 18,
    "metadata": {
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-     "shell.execute_reply": "2024-06-04T04:23:54.452857Z"
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@@ -48304,10 +48304,10 @@
    "execution_count": 19,
    "metadata": {
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-     "shell.execute_reply": "2024-06-04T04:23:54.469516Z"
+     "iopub.execute_input": "2024-06-10T23:56:16.951044Z",
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@@ -48344,10 +48344,10 @@
    "execution_count": 20,
    "metadata": {
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-     "shell.execute_reply": "2024-06-04T04:23:54.829505Z"
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@@ -48373,17 +48373,17 @@
    "execution_count": 21,
    "metadata": {
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-     "shell.execute_reply": "2024-06-04T04:23:55.168367Z"
+     "iopub.execute_input": "2024-06-10T23:56:17.250643Z",
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+     "shell.execute_reply": "2024-06-10T23:56:17.465105Z"
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     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8eac4da9d7254145bafeed0fea3d8f3f",
+       "model_id": "acc4e877968349a387a19fb2f0883df5",
        "version_major": 2,
        "version_minor": 0
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@@ -48418,10 +48418,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:23:55.177942Z",
-     "iopub.status.busy": "2024-06-04T04:23:55.176557Z",
-     "iopub.status.idle": "2024-06-04T04:24:03.723098Z",
-     "shell.execute_reply": "2024-06-04T04:24:03.721334Z"
+     "iopub.execute_input": "2024-06-10T23:56:17.474686Z",
+     "iopub.status.busy": "2024-06-10T23:56:17.473222Z",
+     "iopub.status.idle": "2024-06-10T23:56:20.566386Z",
+     "shell.execute_reply": "2024-06-10T23:56:20.564686Z"
     }
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@@ -48444,10 +48444,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:24:03.731499Z",
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-     "iopub.status.idle": "2024-06-04T04:24:03.742240Z",
-     "shell.execute_reply": "2024-06-04T04:24:03.740521Z"
+     "iopub.execute_input": "2024-06-10T23:56:20.574014Z",
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+     "shell.execute_reply": "2024-06-10T23:56:20.582991Z"
     }
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@@ -48487,10 +48487,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2024-06-04T04:24:03.748660Z",
-     "iopub.status.idle": "2024-06-04T04:26:45.681312Z",
-     "shell.execute_reply": "2024-06-04T04:26:45.679366Z"
+     "iopub.execute_input": "2024-06-10T23:56:20.591225Z",
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@@ -48498,42 +48498,42 @@
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      "output_type": "stream",
      "text": [
-      "2024-06-04 00:26:45,562 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 0\n"
+      "2024-06-10 19:58:36,797 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 1\n"
      ]
     },
     {
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      "output_type": "stream",
      "text": [
-      "2024-06-04 00:26:45,583 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 250\n"
+      "2024-06-10 19:58:36,812 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 500\n"
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      "output_type": "stream",
      "text": [
-      "2024-06-04 00:26:45,584 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
+      "2024-06-10 19:58:36,814 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:26:45,586 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
+      "2024-06-10 19:58:36,816 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:26:45,587 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 1\n"
+      "2024-06-10 19:58:36,817 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:26:45,588 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 1\n"
+      "2024-06-10 19:58:36,818 \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=0, 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=0, 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=0, 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": "2024-06-04T04:26:45.768975Z",
-     "iopub.status.busy": "2024-06-04T04:26:45.767884Z",
-     "iopub.status.idle": "2024-06-04T04:26:45.780241Z",
-     "shell.execute_reply": "2024-06-04T04:26:45.778401Z"
+     "iopub.execute_input": "2024-06-10T23:58:36.884940Z",
+     "iopub.status.busy": "2024-06-10T23:58:36.883131Z",
+     "iopub.status.idle": "2024-06-10T23:58:36.894229Z",
+     "shell.execute_reply": "2024-06-10T23:58:36.892351Z"
     }
    },
    "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': 0, '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': 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': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'seed': 123}\n"
      ]
     }
    ],
@@ -49041,10 +49041,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:26:45.794141Z",
-     "iopub.status.busy": "2024-06-04T04:26:45.793301Z",
-     "iopub.status.idle": "2024-06-04T04:26:45.843725Z",
-     "shell.execute_reply": "2024-06-04T04:26:45.842003Z"
+     "iopub.execute_input": "2024-06-10T23:58:36.901302Z",
+     "iopub.status.busy": "2024-06-10T23:58:36.900200Z",
+     "iopub.status.idle": "2024-06-10T23:58:36.909834Z",
+     "shell.execute_reply": "2024-06-10T23:58:36.908040Z"
     }
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    "outputs": [],
@@ -49066,17 +49066,17 @@
    "execution_count": 27,
    "metadata": {
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-     "iopub.execute_input": "2024-06-04T04:26:45.852470Z",
-     "iopub.status.busy": "2024-06-04T04:26:45.851676Z",
-     "iopub.status.idle": "2024-06-04T04:26:48.792907Z",
-     "shell.execute_reply": "2024-06-04T04:26:48.791093Z"
+     "iopub.execute_input": "2024-06-10T23:58:36.917104Z",
+     "iopub.status.busy": "2024-06-10T23:58:36.915720Z",
+     "iopub.status.idle": "2024-06-10T23:58:39.919624Z",
+     "shell.execute_reply": "2024-06-10T23:58:39.917664Z"
     }
    },
    "outputs": [
     {
      "data": {
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-       "model_id": "a871ff6534124c079c020c5f67712731",
+       "model_id": "eeefbd94677548969f02261c7fe8b4a8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -49122,10 +49122,10 @@
    "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:26:48.803602Z",
-     "iopub.status.busy": "2024-06-04T04:26:48.802604Z",
-     "iopub.status.idle": "2024-06-04T04:26:48.832614Z",
-     "shell.execute_reply": "2024-06-04T04:26:48.830916Z"
+     "iopub.execute_input": "2024-06-10T23:58:39.928112Z",
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+     "shell.execute_reply": "2024-06-10T23:58:39.955255Z"
     }
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@@ -49158,10 +49158,10 @@
    "execution_count": 29,
    "metadata": {
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-     "iopub.execute_input": "2024-06-04T04:26:48.839651Z",
-     "iopub.status.busy": "2024-06-04T04:26:48.838667Z",
-     "iopub.status.idle": "2024-06-04T04:26:48.849879Z",
-     "shell.execute_reply": "2024-06-04T04:26:48.848164Z"
+     "iopub.execute_input": "2024-06-10T23:58:39.964857Z",
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+     "shell.execute_reply": "2024-06-10T23:58:39.973787Z"
     }
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@@ -49201,10 +49201,10 @@
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-     "iopub.status.busy": "2024-06-04T04:26:48.855876Z",
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-     "shell.execute_reply": "2024-06-04T04:26:48.873340Z"
+     "iopub.execute_input": "2024-06-10T23:58:39.983269Z",
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@@ -49235,10 +49235,10 @@
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    "metadata": {
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-     "shell.execute_reply": "2024-06-04T04:26:48.888627Z"
+     "iopub.execute_input": "2024-06-10T23:58:40.009592Z",
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@@ -49266,17 +49266,17 @@
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-     "shell.execute_reply": "2024-06-04T04:26:56.981973Z"
+     "iopub.execute_input": "2024-06-10T23:58:40.025524Z",
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+     "shell.execute_reply": "2024-06-10T23:58:48.112835Z"
     }
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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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@@ -83345,10 +83521,10 @@
    "execution_count": 40,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:26:57.834931Z",
-     "iopub.status.busy": "2024-06-04T04:26:57.833995Z",
-     "iopub.status.idle": "2024-06-04T04:26:57.842438Z",
-     "shell.execute_reply": "2024-06-04T04:26:57.840867Z"
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@@ -83366,10 +83542,10 @@
    "execution_count": 41,
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2024-06-04T04:26:57.848343Z",
-     "iopub.status.idle": "2024-06-04T04:26:57.907308Z",
-     "shell.execute_reply": "2024-06-04T04:26:57.905737Z"
+     "iopub.execute_input": "2024-06-10T23:58:48.885336Z",
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+     "iopub.status.idle": "2024-06-10T23:58:48.941915Z",
+     "shell.execute_reply": "2024-06-10T23:58:48.940245Z"
     }
    },
    "outputs": [
@@ -83391,12 +83567,12 @@
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+          0.17391304671764374,
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@@ -84233,9 +84409,9 @@
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@@ -84283,10 +84459,10 @@
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-     "shell.execute_reply": "2024-06-04T04:26:57.922336Z"
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@@ -84307,10 +84483,10 @@
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 </div>
 
@@ -1485,7 +1485,7 @@ <h3>Version</h3>
         
           
             
-              <b>Date:</b> 2024-06-04
+              <b>Date:</b> 2024-06-10
             
           
           <br>
@@ -1708,20 +1708,24 @@ <h2 class="collapsible">Model Parameters</h2>
 
   
     
+    <div class="subcard">
+      <h3>Random_state</h3>
+      123
+    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Reg_lambda</h3>
-      0
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Eval_metric</h3>
+      logloss
+    </div>
     
   
 
@@ -1732,6 +1736,10 @@ <h3>Reg_lambda</h3>
 
   
     
+    <div class="subcard">
+      <h3>Seed</h3>
+      123
+    </div>
     
   
 
@@ -1739,7 +1747,7 @@ <h3>Reg_lambda</h3>
     
     <div class="subcard">
       <h3>Gamma</h3>
-      1
+      0
     </div>
     
   
@@ -1751,15 +1759,15 @@ <h3>Gamma</h3>
 
   
     
-    <div class="subcard">
-      <h3>N_estimators</h3>
-      250
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Reg_lambda</h3>
+      1
+    </div>
     
   
 
@@ -1775,15 +1783,15 @@ <h3>N_estimators</h3>
 
   
     
-    <div class="subcard">
-      <h3>Learning_rate</h3>
-      0.1
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Max_depth</h3>
+      5
+    </div>
     
   
 
@@ -1804,6 +1812,10 @@ <h3>Learning_rate</h3>
 
   
     
+    <div class="subcard">
+      <h3>Colsample_bytree</h3>
+      0.7
+    </div>
     
   
 
@@ -1814,10 +1826,6 @@ <h3>Learning_rate</h3>
 
   
     
-    <div class="subcard">
-      <h3>Min_child_weight</h3>
-      3
-    </div>
     
   
 
@@ -1828,10 +1836,6 @@ <h3>Min_child_weight</h3>
 
   
     
-    <div class="subcard">
-      <h3>Max_depth</h3>
-      5
-    </div>
     
   
 
@@ -1852,29 +1856,33 @@ <h3>Max_depth</h3>
 
   
     
+    <div class="subcard">
+      <h3>Learning_rate</h3>
+      0.1
+    </div>
     
   
 
   
     
     <div class="subcard">
-      <h3>Colsample_bytree</h3>
-      1
+      <h3>Enable_categorical</h3>
+      False
     </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Missing</h3>
-      nan
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Objective</h3>
+      binary:logistic
+    </div>
     
   
 
@@ -1891,18 +1899,14 @@ <h3>Missing</h3>
   
     
     <div class="subcard">
-      <h3>Eval_metric</h3>
-      logloss
+      <h3>Min_child_weight</h3>
+      3
     </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Random_state</h3>
-      123
-    </div>
     
   
 
@@ -1913,10 +1917,6 @@ <h3>Random_state</h3>
 
   
     
-    <div class="subcard">
-      <h3>Enable_categorical</h3>
-      False
-    </div>
     
   
 
@@ -1932,29 +1932,29 @@ <h3>Enable_categorical</h3>
 
   
     
-    <div class="subcard">
-      <h3>Seed</h3>
-      123
-    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Objective</h3>
-      binary:logistic
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>N_estimators</h3>
+      500
+    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Missing</h3>
+      nan
+    </div>
     
   
 
@@ -2278,9 +2278,9 @@ <h3>Ethical Considerations</h3>
       }
   }
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+  var histories_all = JSON.parse("{\"0\": [\"0.9915611743927002\", \"0.9444175603379927\", \"0.7443490417121632\", \"0.6254756354065529\", \"0.5707621680408038\"], \"1\": [\"0.0\", \"0.19649198621800865\", \"0.2184772597210356\", \"0.30433485831715046\", \"0.30362377852137085\"], \"2\": [\"0.0\", \"0.0\", \"0.18350499787200372\", \"0.28209634314524057\", \"0.2396889726972882\"], \"3\": [\"0.0\", \"0.00402048773810791\", \"0.0\", \"0.013985541191906821\", \"0.1074849892812601\"], \"4\": [\"0.9312617778778076\", \"0.880384872256772\", \"0.9045445288053234\", \"0.8369288131923907\", \"0.7825118730803654\"], \"5\": [\"0.0657845214009285\", \"0.14234660481424422\", \"0.0576100637460394\", \"0.0\", \"0.125912934261293\"], \"6\": [\"0.9722050428390503\", \"1.0\", \"1.0\", \"0.8211644244511235\", \"0.7943329367618954\"], \"7\": [\"0.1599999964237213\", \"0.22196417299727877\", \"0.2776001407252181\", \"0.24922893336778063\", \"0.20797534763959696\"], \"8\": [\"0.1428571492433548\", \"0.09164907525045796\", \"0.1308830007025953\", \"0.2589866563437097\", \"0.26886649326244055\"], \"9\": [\"0.15094339847564697\", \"0.0\", \"0.0\", \"0.024615559359114195\", \"0.0\"], \"10\": [\"0.8173205852508545\", \"0.6844102510597939\", \"0.6888885577772242\", \"0.5739388033895316\", \"0.6579909260618473\"], \"11\": [\"0.11819420009851456\", \"0.04566481355404717\", \"0.0\", \"0.09166388285694961\", \"0.13102015585326207\"], \"12\": [\"0.965976357460022\", \"0.9025779169266341\", \"0.9511258429474247\", \"1.0\", \"0.9293813301661333\"], \"13\": [\"0.1785714328289032\", \"0.2507631184708949\", \"0.1686815718168871\", \"0.11468271731635346\", \"0.06416414237313706\"], \"14\": [\"0.1785714328289032\", \"0.10323209666818883\", \"0.10286089019070693\", \"0.24383303097774947\", \"0.23534418649080502\"], \"15\": [\"0.1785714328289032\", \"0.22326804843198747\", \"0.3737438083682565\", \"0.4758786288048877\", \"0.16177139069150204\"], \"16\": [\"0.7429326772689819\", \"0.6856286080423633\", \"0.7731444886350187\", \"0.6444501204475461\", \"0.6984074956625923\"], \"17\": [\"0.10786601155996323\", \"0.1748322508931597\", \"0.1930680476724418\", \"0.20720807477852732\", \"0.22804283950471124\"], \"18\": [\"0.9800133109092712\", \"0.9765636370900832\", \"1.0\", \"1.0\", \"1.0\"], \"19\": [\"0.23076923191547394\", \"0.4191667822935128\", \"0.4622382787611042\", \"0.6316973221107176\", \"0.6876860056357647\"], \"20\": [\"0.1304347813129425\", \"0.1880894343604082\", \"0.079769290322924\", \"0.02417588738190958\", \"0.0\"], \"21\": [\"0.1666666716337204\", \"0.20576969141015683\", \"0.049294016203655755\", \"0.0\", \"0.0\"], \"22\": [\"0.9102783203125\", \"0.9227906680318644\", \"0.8797299277187282\", \"0.9037571921360225\", \"0.9542442331449171\"], \"23\": [\"0.2032531499862671\", \"0.26650671627784106\", \"0.47494895240973606\", \"0.5455585060558645\", \"0.40589339985108464\"], \"24\": [\"0.9733613729476929\", \"0.775597325032458\", \"0.9517973448389442\", \"0.771016025782727\", \"0.8619900079246055\"], \"25\": [\"0.19512194395065308\", \"0.1844913155395884\", \"0.21206952684485847\", \"0.285466077454379\", \"0.33024172681759345\"], \"26\": [\"0.1568627506494522\", \"0.31944138895030394\", \"0.2382027352514504\", \"0.19326505015452974\", \"0.16029148983452063\"], \"27\": [\"0.17391304671764374\", \"0.22512587988142257\", \"0.15073727181107544\", \"0.15073164285433233\", \"0.1993793459260728\"], \"28\": [\"0.8242921829223633\", \"0.7349572028414906\", \"0.8737046895450459\", \"0.8484390752983139\", \"0.7383920011314941\"], \"29\": [\"0.1339455246925354\", \"0.022696287830978434\", \"0.056073982845798793\", \"0.0004862396180135564\", \"0.0\"]}");
   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\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": false, \"6\": true, \"7\": false, \"8\": false, \"9\": false, \"10\": true, \"11\": false, \"12\": false, \"13\": false, \"14\": false, \"15\": false, \"16\": false, \"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 passed_all = JSON.parse("{\"0\": false, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": false, \"6\": true, \"7\": false, \"8\": false, \"9\": false, \"10\": false, \"11\": false, \"12\": true, \"13\": false, \"14\": false, \"15\": false, \"16\": false, \"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\"]}");
 
@@ -2505,7 +2505,7 @@ <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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@@ -2721,9 +2721,9 @@ <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\": [\"0.9915611743927002\", \"0.9444175603379927\", \"0.7443490417121632\", \"0.6254756354065529\", \"0.5707621680408038\"], \"1\": [\"0.0\", \"0.19649198621800865\", \"0.2184772597210356\", \"0.30433485831715046\", \"0.30362377852137085\"], \"2\": [\"0.0\", \"0.0\", \"0.18350499787200372\", \"0.28209634314524057\", \"0.2396889726972882\"], \"3\": [\"0.0\", \"0.00402048773810791\", \"0.0\", \"0.013985541191906821\", \"0.1074849892812601\"], \"4\": [\"0.9312617778778076\", \"0.880384872256772\", \"0.9045445288053234\", \"0.8369288131923907\", \"0.7825118730803654\"], \"5\": [\"0.0657845214009285\", \"0.14234660481424422\", \"0.0576100637460394\", \"0.0\", \"0.125912934261293\"], \"6\": [\"0.9722050428390503\", \"1.0\", \"1.0\", \"0.8211644244511235\", \"0.7943329367618954\"], \"7\": [\"0.1599999964237213\", \"0.22196417299727877\", \"0.2776001407252181\", \"0.24922893336778063\", \"0.20797534763959696\"], \"8\": [\"0.1428571492433548\", \"0.09164907525045796\", \"0.1308830007025953\", \"0.2589866563437097\", \"0.26886649326244055\"], \"9\": [\"0.15094339847564697\", \"0.0\", \"0.0\", \"0.024615559359114195\", \"0.0\"], \"10\": [\"0.8173205852508545\", \"0.6844102510597939\", \"0.6888885577772242\", \"0.5739388033895316\", \"0.6579909260618473\"], \"11\": [\"0.11819420009851456\", \"0.04566481355404717\", \"0.0\", \"0.09166388285694961\", \"0.13102015585326207\"], \"12\": [\"0.965976357460022\", \"0.9025779169266341\", \"0.9511258429474247\", \"1.0\", \"0.9293813301661333\"], \"13\": [\"0.1785714328289032\", \"0.2507631184708949\", \"0.1686815718168871\", \"0.11468271731635346\", \"0.06416414237313706\"], \"14\": [\"0.1785714328289032\", \"0.10323209666818883\", \"0.10286089019070693\", \"0.24383303097774947\", \"0.23534418649080502\"], \"15\": [\"0.1785714328289032\", \"0.22326804843198747\", \"0.3737438083682565\", \"0.4758786288048877\", \"0.16177139069150204\"], \"16\": [\"0.7429326772689819\", \"0.6856286080423633\", \"0.7731444886350187\", \"0.6444501204475461\", \"0.6984074956625923\"], \"17\": [\"0.10786601155996323\", \"0.1748322508931597\", \"0.1930680476724418\", \"0.20720807477852732\", \"0.22804283950471124\"], \"18\": [\"0.9800133109092712\", \"0.9765636370900832\", \"1.0\", \"1.0\", \"1.0\"], \"19\": [\"0.23076923191547394\", \"0.4191667822935128\", \"0.4622382787611042\", \"0.6316973221107176\", \"0.6876860056357647\"], \"20\": [\"0.1304347813129425\", \"0.1880894343604082\", \"0.079769290322924\", \"0.02417588738190958\", \"0.0\"], \"21\": [\"0.1666666716337204\", \"0.20576969141015683\", \"0.049294016203655755\", \"0.0\", \"0.0\"], \"22\": [\"0.9102783203125\", \"0.9227906680318644\", \"0.8797299277187282\", \"0.9037571921360225\", \"0.9542442331449171\"], \"23\": [\"0.2032531499862671\", \"0.26650671627784106\", \"0.47494895240973606\", \"0.5455585060558645\", \"0.40589339985108464\"], \"24\": [\"0.9733613729476929\", \"0.775597325032458\", \"0.9517973448389442\", \"0.771016025782727\", \"0.8619900079246055\"], \"25\": [\"0.19512194395065308\", \"0.1844913155395884\", \"0.21206952684485847\", \"0.285466077454379\", \"0.33024172681759345\"], \"26\": [\"0.1568627506494522\", \"0.31944138895030394\", \"0.2382027352514504\", \"0.19326505015452974\", \"0.16029148983452063\"], \"27\": [\"0.17391304671764374\", \"0.22512587988142257\", \"0.15073727181107544\", \"0.15073164285433233\", \"0.1993793459260728\"], \"28\": [\"0.8242921829223633\", \"0.7349572028414906\", \"0.8737046895450459\", \"0.8484390752983139\", \"0.7383920011314941\"], \"29\": [\"0.1339455246925354\", \"0.022696287830978434\", \"0.056073982845798793\", \"0.0004862396180135564\", \"0.0\"]}");
   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\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": false, \"6\": true, \"7\": false, \"8\": false, \"9\": false, \"10\": true, \"11\": false, \"12\": false, \"13\": false, \"14\": false, \"15\": false, \"16\": false, \"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 passed_all = JSON.parse("{\"0\": false, \"1\": false, \"2\": false, \"3\": false, \"4\": true, \"5\": false, \"6\": true, \"7\": false, \"8\": false, \"9\": false, \"10\": false, \"11\": false, \"12\": true, \"13\": false, \"14\": false, \"15\": false, \"16\": false, \"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\"]}");
 
@@ -3122,7 +3122,7 @@ <h3>Ethical Considerations</h3>
   ];
 
   // create global variable for max_n_evals
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   // get max_n_evals from histories
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index cec573434..fd6e9c76d 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 7e801fb02..1fa7b5bcb 100644
--- a/api/tutorials/nihcxr/cxr_classification.ipynb
+++ b/api/tutorials/nihcxr/cxr_classification.ipynb
@@ -24,10 +24,10 @@
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@@ -69,10 +69,10 @@
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-     "shell.execute_reply": "2024-06-04T04:51:37.190264Z"
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@@ -90,8 +90,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Flattening the indices: 100%|########| 1000/1000 [03:51<00:00,  4.31 examples/s]\r",
-      "Flattening the indices: 100%|########| 1000/1000 [03:51<00:00,  4.31 examples/s]\r\n"
+      "Flattening the indices: 100%|########| 1000/1000 [03:53<00:00,  4.28 examples/s]"
      ]
     },
     {
@@ -99,10 +98,10 @@
      "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, 167363.79 examples/s]\r\n",
+      "Flattening the indices: 100%|########| 1000/1000 [03:53<00:00,  4.28 examples/s]\r\n",
       "\r",
-      "Filter:   0%|                                   | 0/1000 [00:00<?, ? examples/s]"
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@@ -120,7 +120,7 @@
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      ]
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      ]
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      ]
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@@ -190,14 +189,15 @@
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      ]
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     {
      "name": "stdout",
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       "\r",
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@@ -249,7 +249,7 @@
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+     ]
+    },
+    {
+     "name": "stdout",
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+     "text": [
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@@ -372,16 +379,18 @@
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      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
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@@ -411,9 +420,7 @@
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      ]
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@@ -421,10 +428,11 @@
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+     ]
+    },
+    {
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@@ -490,7 +504,7 @@
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      ]
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      ]
     },
     {
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      "output_type": "stream",
      "text": [
       "\r",
-      "Map: 100%|############################| 411/411 [00:00<00:00, 742.85 examples/s]\r\n"
+      "Map: 100%|############################| 411/411 [00:00<00:00, 586.05 examples/s]\r\n"
      ]
     },
     {
@@ -667,7 +681,7 @@
      "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, 38978.41 examples/s]\r\n"
+      "Filter -> Patient Gender:M: 100%|###| 411/411 [00:00<00:00, 31131.76 examples/s]\r\n"
      ]
     },
     {
@@ -675,7 +689,8 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Gender:F:   0%|                | 0/411 [00:00<?, ? examples/s]"
+      "Filter -> Patient Gender:F:   0%|                | 0/411 [00:00<?, ? examples/s]\r",
+      "Filter -> Patient Gender:F: 100%|###| 411/411 [00:00<00:00, 36261.99 examples/s]\r\n"
      ]
     },
     {
@@ -683,9 +698,8 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Gender:F: 100%|###| 411/411 [00:00<00:00, 40138.28 examples/s]\r\n",
-      "\r",
-      "Filter -> overall:   0%|                         | 0/411 [00:00<?, ? examples/s]"
+      "Filter -> overall:   0%|                         | 0/411 [00:00<?, ? examples/s]\r",
+      "Filter -> overall: 100%|############| 411/411 [00:00<00:00, 39648.99 examples/s]\r\n"
      ]
     },
     {
@@ -693,7 +707,8 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> overall: 100%|############| 411/411 [00:00<00:00, 43456.07 examples/s]\r\n"
+      "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, 31411.42 examples\r\n"
      ]
     },
     {
@@ -701,10 +716,8 @@
      "output_type": "stream",
      "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, 32109.43 examples\r\n",
-      "\r",
-      "Filter -> Patient Age:[35 - 65]:   0%|           | 0/411 [00:00<?, ? examples/s]"
+      "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, 32888.66 examples\r\n"
      ]
     },
     {
@@ -712,7 +725,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:[35 - 65]: 100%|#| 411/411 [00:00<00:00, 33187.51 examples\r\n"
+      "Filter -> Patient Age:[65 - 100]:   0%|          | 0/411 [00:00<?, ? examples/s]"
      ]
     },
     {
@@ -720,8 +733,9 @@
      "output_type": "stream",
      "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, 33491.85 example\r\n"
+      "Filter -> Patient Age:[65 - 100]: 100%|#| 411/411 [00:00<00:00, 32829.78 example\r\n",
+      "\r",
+      "Filter -> Patient Age:[19 - 35]&Patient Gender:M:   0%| | 0/411 [00:00<?, ? exam"
      ]
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     {
@@ -729,10 +743,10 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:[19 - 35]&Patient Gender:M:   0%| | 0/411 [00:00<?, ? exam\r",
       "Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|#| 411/411 [00:00<00:00, \r\n",
       "\r",
-      "Filter -> Patient Age:[19 - 35]&Patient Gender:F:   0%| | 0/411 [00:00<?, ? exam"
+      "Filter -> Patient Age:[19 - 35]&Patient Gender:F:   0%| | 0/411 [00:00<?, ? exam\r",
+      "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|#| 411/411 [00:00<00:00, \r\n"
      ]
     },
     {
@@ -740,17 +754,14 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|#| 411/411 [00:00<00:00, \r\n",
-      "\r",
-      "Filter -> Patient Age:[35 - 65]&Patient Gender:M:   0%| | 0/411 [00:00<?, ? exam"
+      "Filter -> Patient Age:[35 - 65]&Patient Gender:M:   0%| | 0/411 [00:00<?, ? exam\r",
+      "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|#| 411/411 [00:00<00:00, \r\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
-      "Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|#| 411/411 [00:00<00:00, \r\n",
       "\r",
       "Filter -> Patient Age:[35 - 65]&Patient Gender:F:   0%| | 0/411 [00:00<?, ? exam\r",
       "Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|#| 411/411 [00:00<00:00, \r\n"
@@ -761,18 +772,19 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> Patient Age:[65 - 100]&Patient Gender:M:   0%| | 0/411 [00:00<?, ? exa"
+      "Filter -> Patient Age:[65 - 100]&Patient Gender:M:   0%| | 0/411 [00:00<?, ? exa\r",
+      "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|#| 411/411 [00:00<00:00,\r\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
-      "Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|#| 411/411 [00:00<00:00,\r\n",
       "\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]"
      ]
     },
     {
@@ -780,8 +792,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Filter -> overall:   0%|                         | 0/411 [00:00<?, ? examples/s]\r",
-      "Filter -> overall: 100%|############| 411/411 [00:00<00:00, 41394.14 examples/s]\r\n"
+      "Filter -> overall: 100%|############| 411/411 [00:00<00:00, 41096.12 examples/s]\r\n"
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@@ -829,10 +840,10 @@
    "id": "03edf1c0",
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-     "shell.execute_reply": "2024-06-04T04:51:37.212836Z"
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@@ -854,10 +865,10 @@
    "id": "6514120e",
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-     "shell.execute_reply": "2024-06-04T04:51:56.149341Z"
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@@ -901,17 +912,17 @@
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-     "shell.execute_reply": "2024-06-04T04:52:30.555366Z"
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@@ -925,7 +936,7 @@
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@@ -985,17 +996,17 @@
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-     "shell.execute_reply": "2024-06-04T04:52:30.999065Z"
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@@ -1009,7 +1020,7 @@
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@@ -1023,7 +1034,7 @@
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@@ -1101,17 +1112,17 @@
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-     "shell.execute_reply": "2024-06-04T04:52:31.919329Z"
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+     "shell.execute_reply": "2024-06-11T00:23:55.175078Z"
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@@ -1125,7 +1136,7 @@
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@@ -1139,7 +1150,7 @@
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@@ -1153,7 +1164,7 @@
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@@ -1167,7 +1178,7 @@
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@@ -1181,7 +1192,7 @@
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@@ -1209,7 +1220,7 @@
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@@ -1237,7 +1248,7 @@
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@@ -1300,10 +1311,10 @@
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-     "shell.execute_reply": "2024-06-04T04:52:32.629560Z"
+     "iopub.execute_input": "2024-06-11T00:23:55.184443Z",
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@@ -2198,9 +2209,9 @@
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@@ -2254,10 +2265,10 @@
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@@ -4842,10 +4853,10 @@
    "id": "ec627997",
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-     "iopub.status.idle": "2024-06-04T04:52:37.722357Z",
-     "shell.execute_reply": "2024-06-04T04:52:37.720356Z"
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@@ -4903,10 +4914,10 @@
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+    <div>                            <div id="2c5fbe20-9c3b-454f-a29d-3da87dc37510" class="plotly-graph-div" style="height:100%; width:100%;"></div>            <script type="text/javascript">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("2c5fbe20-9c3b-454f-a29d-3da87dc37510")) {                    Plotly.newPlot(                        "2c5fbe20-9c3b-454f-a29d-3da87dc37510",                        [{"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>
 
@@ -991,7 +991,7 @@ <h3>Graphics</h3>
     
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-        <span style="font-size: 48px; font-weight:bolder; color: green">0.88</span>
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 </div>
 
@@ -1387,7 +1387,7 @@ <h3>Version</h3>
         
           
             
-              <b>Date:</b> 2024-06-04
+              <b>Date:</b> 2024-06-10
             
           
           <br>
@@ -1611,8 +1611,8 @@ <h2 class="collapsible">Model Parameters</h2>
   
     
     <div class="subcard">
-      <h3>N_estimators</h3>
-      250
+      <h3>Colsample_bytree</h3>
+      0.7
     </div>
     
   
@@ -1624,10 +1624,6 @@ <h3>N_estimators</h3>
 
   
     
-    <div class="subcard">
-      <h3>Colsample_bytree</h3>
-      1
-    </div>
     
   
 
@@ -1638,19 +1634,11 @@ <h3>Colsample_bytree</h3>
 
   
     
-    <div class="subcard">
-      <h3>Enable_categorical</h3>
-      False
-    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Min_child_weight</h3>
-      3
-    </div>
     
   
 
@@ -1666,6 +1654,10 @@ <h3>Min_child_weight</h3>
 
   
     
+    <div class="subcard">
+      <h3>Eval_metric</h3>
+      logloss
+    </div>
     
   
 
@@ -1681,6 +1673,10 @@ <h3>Min_child_weight</h3>
 
   
     
+    <div class="subcard">
+      <h3>Learning_rate</h3>
+      0.1
+    </div>
     
   
 
@@ -1696,55 +1692,47 @@ <h3>Min_child_weight</h3>
 
   
     
-    <div class="subcard">
-      <h3>Random_state</h3>
-      123
-    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Eval_metric</h3>
-      logloss
-    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Objective</h3>
-      binary:logistic
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>N_estimators</h3>
+      100
+    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Seed</h3>
+      123
+    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Learning_rate</h3>
-      0.1
-    </div>
     
   
 
   
     
     <div class="subcard">
-      <h3>Reg_lambda</h3>
-      10
+      <h3>Enable_categorical</h3>
+      False
     </div>
     
   
@@ -1762,8 +1750,8 @@ <h3>Reg_lambda</h3>
   
     
     <div class="subcard">
-      <h3>Gamma</h3>
-      10
+      <h3>Max_depth</h3>
+      2
     </div>
     
   
@@ -1785,15 +1773,15 @@ <h3>Gamma</h3>
 
   
     
-    <div class="subcard">
-      <h3>Max_depth</h3>
-      5
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Objective</h3>
+      binary:logistic
+    </div>
     
   
 
@@ -1805,8 +1793,8 @@ <h3>Max_depth</h3>
   
     
     <div class="subcard">
-      <h3>Seed</h3>
-      123
+      <h3>Missing</h3>
+      nan
     </div>
     
   
@@ -1823,20 +1811,24 @@ <h3>Seed</h3>
 
   
     
+    <div class="subcard">
+      <h3>Reg_lambda</h3>
+      1
+    </div>
     
   
 
   
     
-    <div class="subcard">
-      <h3>Missing</h3>
-      nan
-    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Random_state</h3>
+      123
+    </div>
     
   
 
@@ -1847,11 +1839,19 @@ <h3>Missing</h3>
 
   
     
+    <div class="subcard">
+      <h3>Gamma</h3>
+      1
+    </div>
     
   
 
   
     
+    <div class="subcard">
+      <h3>Min_child_weight</h3>
+      3
+    </div>
     
   
 
@@ -2180,9 +2180,9 @@ <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\": [\"0.8333333134651184\", \"0.8094662738308168\", \"0.8595458420662222\", \"0.9296908013627551\", \"1.0\"], \"1\": [\"0.9375\", \"0.9024813497004001\", \"0.8455642329624111\", \"0.7874311554591716\", \"0.8345889279760974\"], \"2\": [\"0.725806474685669\", \"0.8907857631730102\", \"0.776170444479959\", \"0.6162736849512381\", \"0.5839857016862101\"], \"3\": [\"0.8181818127632141\", \"0.8391178204004882\", \"0.8189110064644611\", \"0.7800686190826583\", \"0.7491002482285714\"], \"4\": [\"0.92046719789505\", \"0.9380071097762551\", \"0.947841009833929\", \"0.8667618645131204\", \"0.7170302376680323\"], \"5\": [\"0.942307710647583\", \"0.9694386258446315\", \"0.951919388633946\", \"0.9587879289048926\", \"1.0\"], \"6\": [\"0.970588207244873\", \"1.0\", \"0.9557762167388503\", \"0.8805875340586622\", \"0.747940294459579\"], \"7\": [\"0.9428571462631226\", \"0.8783779245235137\", \"0.8261186611612624\", \"0.7643053688953335\", \"0.5512282749864735\"], \"8\": [\"0.95652174949646\", \"0.8192877019173987\", \"0.8426576551838371\", \"0.9074657468620512\", \"0.9592384385038815\"], \"9\": [\"0.9529411792755127\", \"0.8798741723624461\", \"0.985830182978628\", \"0.9701480666125963\", \"0.9273903866439045\"], \"10\": [\"0.9359999895095825\", \"0.762792327372465\", \"0.7281058084401141\", \"0.831165238383092\", \"0.7764860309058591\"], \"11\": [\"1.0\", \"0.9850487289923939\", \"0.9518471498765199\", \"0.9670909080594012\", \"0.8751115995960776\"], \"12\": [\"0.9080459475517273\", \"0.8065706207296807\", \"0.6420965804007145\", \"0.6565092564864992\", \"0.47373432709702756\"], \"13\": [\"0.9518072009086609\", \"1.0\", \"1.0\", \"0.9824250927878341\", \"1.0\"], \"14\": [\"0.9838172793388367\", \"1.0\", \"0.8162290679117923\", \"0.7423937230911423\", \"0.7490988849771455\"], \"15\": [\"0.8217821717262268\", \"0.7287226371022525\", \"0.7150795125565077\", \"0.5491627294294421\", \"0.4008606031241557\"], \"16\": [\"0.8999999761581421\", \"0.9338681042203166\", \"0.9317241579241186\", \"1.0\", \"0.9078432043840289\"], \"17\": [\"0.7758620977401733\", \"0.8123644285359083\", \"0.7346746694626879\", \"0.7444220062324121\", \"0.8046637991320977\"], \"18\": [\"0.8333333134651184\", \"0.9964887386981487\", \"1.0\", \"0.9019569094861815\", \"0.6958002163405863\"], \"19\": [\"0.9109863638877869\", \"0.9246858504657817\", \"0.9015461561107054\", \"1.0\", \"1.0\"], \"20\": [\"0.8849557638168335\", \"0.9869658878824109\", \"1.0\", \"1.0\", \"1.0\"], \"21\": [\"0.961240291595459\", \"0.9841299232764547\", \"0.8977064505958645\", \"0.933054387411553\", \"0.8860241272457073\"], \"22\": [\"0.8551723957061768\", \"0.903950970086637\", \"1.0\", \"1.0\", \"0.9407837714504604\"], \"23\": [\"0.9051094651222229\", \"0.8992678516864064\", \"1.0\", \"0.8821243872887108\", \"0.8297785626156888\"], \"24\": [\"0.9511281847953796\", \"0.8302767893991136\", \"0.9074862305526562\", \"0.9049898951773042\", \"0.8820971497325474\"]}");
+  var histories_all = JSON.parse("{\"0\": [\"0.8333333134651184\", \"0.8587494487475286\", \"0.8257740602552828\", \"0.7028458044560064\", \"0.719231415204748\"], \"1\": [\"0.9583333134651184\", \"1.0\", \"1.0\", \"0.9595886982944456\", \"0.8456794214576862\"], \"2\": [\"0.7301587462425232\", \"0.6776819927198064\", \"0.7422882023613603\", \"0.6405926551643425\", \"0.5416789763424679\"], \"3\": [\"0.8288288116455078\", \"0.8946489212523333\", \"0.8568053182924886\", \"1.0\", \"1.0\"], \"4\": [\"0.9304388761520386\", \"1.0\", \"1.0\", \"0.9770146350190764\", \"1.0\"], \"5\": [\"0.9272727370262146\", \"0.8185682862892766\", \"0.7343316812356244\", \"0.5741541243871904\", \"0.44604741468587494\"], \"6\": [\"1.0\", \"1.0\", \"1.0\", \"0.9409442634495603\", \"0.9567972808983531\"], \"7\": [\"0.875\", \"0.9337626822366054\", \"0.9114279365305047\", \"1.0\", \"0.9420995692860883\"], \"8\": [\"0.9333333373069763\", \"0.8390371680917077\", \"0.8797118220534562\", \"0.8576076266752097\", \"0.8353335312464731\"], \"9\": [\"0.9748641848564148\", \"0.9793806822211983\", \"0.8747198096182981\", \"0.8417577383377725\", \"0.7924438732183218\"], \"10\": [\"0.8938053250312805\", \"0.945187396641226\", \"1.0\", \"1.0\", \"1.0\"], \"11\": [\"0.9852941036224365\", \"0.940773544565984\", \"1.0\", \"1.0\", \"0.9744433197081129\"], \"12\": [\"0.8589743375778198\", \"0.8865610194071949\", \"0.7586891415039964\", \"0.8971624873325016\", \"1.0\"], \"13\": [\"0.9178082346916199\", \"0.8598572506782375\", \"0.7585091748037436\", \"0.5311631320652456\", \"0.4961215285760771\"], \"14\": [\"0.9622710347175598\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.8938053250312805\", \"1.0\", \"0.8443982220470327\", \"0.8958046283624884\", \"0.8927867880071723\"], \"16\": [\"0.9661017060279846\", \"1.0\", \"0.9323038228091215\", \"1.0\", \"1.0\"], \"17\": [\"0.8507462739944458\", \"1.0\", \"1.0\", \"0.9549682369985835\", \"0.8843749365106865\"], \"18\": [\"0.9047619104385376\", \"0.7023552195467943\", \"0.6658228332690145\", \"0.6790478087914034\", \"0.6508387841142056\"], \"19\": [\"0.962199866771698\", \"1.0\", \"1.0\", \"0.9543924421085003\", \"0.8552515794947849\"], \"20\": [\"0.8938053250312805\", \"0.8894592623262126\", \"0.9876749049248656\", \"1.0\", \"1.0\"], \"21\": [\"0.9763779640197754\", \"0.9845057836238998\", \"0.8378172300427879\", \"0.773924095158208\", \"0.7827441687349376\"], \"22\": [\"0.8551723957061768\", \"0.7536699140630719\", \"0.7850306630873699\", \"0.8913712982873504\", \"0.8401728944711612\"], \"23\": [\"0.9117646813392639\", \"0.9976326350998985\", \"1.0\", \"0.9791517388696462\", \"0.86319274387944\"], \"24\": [\"0.9621540904045105\", \"1.0\", \"1.0\", \"1.0\", \"0.9373681933529148\"]}");
   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\": true, \"7\": false, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": false, \"13\": true, \"14\": true, \"15\": false, \"16\": true, \"17\": true, \"18\": false, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}");
+  var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": false, \"3\": true, \"4\": true, \"5\": false, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": false, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": false, \"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\"]}");
 
@@ -2407,7 +2407,7 @@ <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\": [\"0.8333333134651184\", \"0.8094662738308168\", \"0.8595458420662222\", \"0.9296908013627551\", \"1.0\"], \"1\": [\"0.9375\", \"0.9024813497004001\", \"0.8455642329624111\", \"0.7874311554591716\", \"0.8345889279760974\"], \"2\": [\"0.725806474685669\", \"0.8907857631730102\", \"0.776170444479959\", \"0.6162736849512381\", \"0.5839857016862101\"], \"3\": [\"0.8181818127632141\", \"0.8391178204004882\", \"0.8189110064644611\", \"0.7800686190826583\", \"0.7491002482285714\"], \"4\": [\"0.92046719789505\", \"0.9380071097762551\", \"0.947841009833929\", \"0.8667618645131204\", \"0.7170302376680323\"], \"5\": [\"0.942307710647583\", \"0.9694386258446315\", \"0.951919388633946\", \"0.9587879289048926\", \"1.0\"], \"6\": [\"0.970588207244873\", \"1.0\", \"0.9557762167388503\", \"0.8805875340586622\", \"0.747940294459579\"], \"7\": [\"0.9428571462631226\", \"0.8783779245235137\", \"0.8261186611612624\", \"0.7643053688953335\", \"0.5512282749864735\"], \"8\": [\"0.95652174949646\", \"0.8192877019173987\", \"0.8426576551838371\", \"0.9074657468620512\", \"0.9592384385038815\"], \"9\": [\"0.9529411792755127\", \"0.8798741723624461\", \"0.985830182978628\", \"0.9701480666125963\", \"0.9273903866439045\"], \"10\": [\"0.9359999895095825\", \"0.762792327372465\", \"0.7281058084401141\", \"0.831165238383092\", \"0.7764860309058591\"], \"11\": [\"1.0\", \"0.9850487289923939\", \"0.9518471498765199\", \"0.9670909080594012\", \"0.8751115995960776\"], \"12\": [\"0.9080459475517273\", \"0.8065706207296807\", \"0.6420965804007145\", \"0.6565092564864992\", \"0.47373432709702756\"], \"13\": [\"0.9518072009086609\", \"1.0\", \"1.0\", \"0.9824250927878341\", \"1.0\"], \"14\": [\"0.9838172793388367\", \"1.0\", \"0.8162290679117923\", \"0.7423937230911423\", \"0.7490988849771455\"], \"15\": [\"0.8217821717262268\", \"0.7287226371022525\", \"0.7150795125565077\", \"0.5491627294294421\", \"0.4008606031241557\"], \"16\": [\"0.8999999761581421\", \"0.9338681042203166\", \"0.9317241579241186\", \"1.0\", \"0.9078432043840289\"], \"17\": [\"0.7758620977401733\", \"0.8123644285359083\", \"0.7346746694626879\", \"0.7444220062324121\", \"0.8046637991320977\"], \"18\": [\"0.8333333134651184\", \"0.9964887386981487\", \"1.0\", \"0.9019569094861815\", \"0.6958002163405863\"], \"19\": [\"0.9109863638877869\", \"0.9246858504657817\", \"0.9015461561107054\", \"1.0\", \"1.0\"], \"20\": [\"0.8849557638168335\", \"0.9869658878824109\", \"1.0\", \"1.0\", \"1.0\"], \"21\": [\"0.961240291595459\", \"0.9841299232764547\", \"0.8977064505958645\", \"0.933054387411553\", \"0.8860241272457073\"], \"22\": [\"0.8551723957061768\", \"0.903950970086637\", \"1.0\", \"1.0\", \"0.9407837714504604\"], \"23\": [\"0.9051094651222229\", \"0.8992678516864064\", \"1.0\", \"0.8821243872887108\", \"0.8297785626156888\"], \"24\": [\"0.9511281847953796\", \"0.8302767893991136\", \"0.9074862305526562\", \"0.9049898951773042\", \"0.8820971497325474\"]}");
+  var histories = JSON.parse("{\"0\": [\"0.8333333134651184\", \"0.8587494487475286\", \"0.8257740602552828\", \"0.7028458044560064\", \"0.719231415204748\"], \"1\": [\"0.9583333134651184\", \"1.0\", \"1.0\", \"0.9595886982944456\", \"0.8456794214576862\"], \"2\": [\"0.7301587462425232\", \"0.6776819927198064\", \"0.7422882023613603\", \"0.6405926551643425\", \"0.5416789763424679\"], \"3\": [\"0.8288288116455078\", \"0.8946489212523333\", \"0.8568053182924886\", \"1.0\", \"1.0\"], \"4\": [\"0.9304388761520386\", \"1.0\", \"1.0\", \"0.9770146350190764\", \"1.0\"], \"5\": [\"0.9272727370262146\", \"0.8185682862892766\", \"0.7343316812356244\", \"0.5741541243871904\", \"0.44604741468587494\"], \"6\": [\"1.0\", \"1.0\", \"1.0\", \"0.9409442634495603\", \"0.9567972808983531\"], \"7\": [\"0.875\", \"0.9337626822366054\", \"0.9114279365305047\", \"1.0\", \"0.9420995692860883\"], \"8\": [\"0.9333333373069763\", \"0.8390371680917077\", \"0.8797118220534562\", \"0.8576076266752097\", \"0.8353335312464731\"], \"9\": [\"0.9748641848564148\", \"0.9793806822211983\", \"0.8747198096182981\", \"0.8417577383377725\", \"0.7924438732183218\"], \"10\": [\"0.8938053250312805\", \"0.945187396641226\", \"1.0\", \"1.0\", \"1.0\"], \"11\": [\"0.9852941036224365\", \"0.940773544565984\", \"1.0\", \"1.0\", \"0.9744433197081129\"], \"12\": [\"0.8589743375778198\", \"0.8865610194071949\", \"0.7586891415039964\", \"0.8971624873325016\", \"1.0\"], \"13\": [\"0.9178082346916199\", \"0.8598572506782375\", \"0.7585091748037436\", \"0.5311631320652456\", \"0.4961215285760771\"], \"14\": [\"0.9622710347175598\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.8938053250312805\", \"1.0\", \"0.8443982220470327\", \"0.8958046283624884\", \"0.8927867880071723\"], \"16\": [\"0.9661017060279846\", \"1.0\", \"0.9323038228091215\", \"1.0\", \"1.0\"], \"17\": [\"0.8507462739944458\", \"1.0\", \"1.0\", \"0.9549682369985835\", \"0.8843749365106865\"], \"18\": [\"0.9047619104385376\", \"0.7023552195467943\", \"0.6658228332690145\", \"0.6790478087914034\", \"0.6508387841142056\"], \"19\": [\"0.962199866771698\", \"1.0\", \"1.0\", \"0.9543924421085003\", \"0.8552515794947849\"], \"20\": [\"0.8938053250312805\", \"0.8894592623262126\", \"0.9876749049248656\", \"1.0\", \"1.0\"], \"21\": [\"0.9763779640197754\", \"0.9845057836238998\", \"0.8378172300427879\", \"0.773924095158208\", \"0.7827441687349376\"], \"22\": [\"0.8551723957061768\", \"0.7536699140630719\", \"0.7850306630873699\", \"0.8913712982873504\", \"0.8401728944711612\"], \"23\": [\"0.9117646813392639\", \"0.9976326350998985\", \"1.0\", \"0.9791517388696462\", \"0.86319274387944\"], \"24\": [\"0.9621540904045105\", \"1.0\", \"1.0\", \"1.0\", \"0.9373681933529148\"]}");
   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\"]}");
 
@@ -2623,9 +2623,9 @@ <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\": [\"0.8333333134651184\", \"0.8094662738308168\", \"0.8595458420662222\", \"0.9296908013627551\", \"1.0\"], \"1\": [\"0.9375\", \"0.9024813497004001\", \"0.8455642329624111\", \"0.7874311554591716\", \"0.8345889279760974\"], \"2\": [\"0.725806474685669\", \"0.8907857631730102\", \"0.776170444479959\", \"0.6162736849512381\", \"0.5839857016862101\"], \"3\": [\"0.8181818127632141\", \"0.8391178204004882\", \"0.8189110064644611\", \"0.7800686190826583\", \"0.7491002482285714\"], \"4\": [\"0.92046719789505\", \"0.9380071097762551\", \"0.947841009833929\", \"0.8667618645131204\", \"0.7170302376680323\"], \"5\": [\"0.942307710647583\", \"0.9694386258446315\", \"0.951919388633946\", \"0.9587879289048926\", \"1.0\"], \"6\": [\"0.970588207244873\", \"1.0\", \"0.9557762167388503\", \"0.8805875340586622\", \"0.747940294459579\"], \"7\": [\"0.9428571462631226\", \"0.8783779245235137\", \"0.8261186611612624\", \"0.7643053688953335\", \"0.5512282749864735\"], \"8\": [\"0.95652174949646\", \"0.8192877019173987\", \"0.8426576551838371\", \"0.9074657468620512\", \"0.9592384385038815\"], \"9\": [\"0.9529411792755127\", \"0.8798741723624461\", \"0.985830182978628\", \"0.9701480666125963\", \"0.9273903866439045\"], \"10\": [\"0.9359999895095825\", \"0.762792327372465\", \"0.7281058084401141\", \"0.831165238383092\", \"0.7764860309058591\"], \"11\": [\"1.0\", \"0.9850487289923939\", \"0.9518471498765199\", \"0.9670909080594012\", \"0.8751115995960776\"], \"12\": [\"0.9080459475517273\", \"0.8065706207296807\", \"0.6420965804007145\", \"0.6565092564864992\", \"0.47373432709702756\"], \"13\": [\"0.9518072009086609\", \"1.0\", \"1.0\", \"0.9824250927878341\", \"1.0\"], \"14\": [\"0.9838172793388367\", \"1.0\", \"0.8162290679117923\", \"0.7423937230911423\", \"0.7490988849771455\"], \"15\": [\"0.8217821717262268\", \"0.7287226371022525\", \"0.7150795125565077\", \"0.5491627294294421\", \"0.4008606031241557\"], \"16\": [\"0.8999999761581421\", \"0.9338681042203166\", \"0.9317241579241186\", \"1.0\", \"0.9078432043840289\"], \"17\": [\"0.7758620977401733\", \"0.8123644285359083\", \"0.7346746694626879\", \"0.7444220062324121\", \"0.8046637991320977\"], \"18\": [\"0.8333333134651184\", \"0.9964887386981487\", \"1.0\", \"0.9019569094861815\", \"0.6958002163405863\"], \"19\": [\"0.9109863638877869\", \"0.9246858504657817\", \"0.9015461561107054\", \"1.0\", \"1.0\"], \"20\": [\"0.8849557638168335\", \"0.9869658878824109\", \"1.0\", \"1.0\", \"1.0\"], \"21\": [\"0.961240291595459\", \"0.9841299232764547\", \"0.8977064505958645\", \"0.933054387411553\", \"0.8860241272457073\"], \"22\": [\"0.8551723957061768\", \"0.903950970086637\", \"1.0\", \"1.0\", \"0.9407837714504604\"], \"23\": [\"0.9051094651222229\", \"0.8992678516864064\", \"1.0\", \"0.8821243872887108\", \"0.8297785626156888\"], \"24\": [\"0.9511281847953796\", \"0.8302767893991136\", \"0.9074862305526562\", \"0.9049898951773042\", \"0.8820971497325474\"]}");
+  var histories_all = JSON.parse("{\"0\": [\"0.8333333134651184\", \"0.8587494487475286\", \"0.8257740602552828\", \"0.7028458044560064\", \"0.719231415204748\"], \"1\": [\"0.9583333134651184\", \"1.0\", \"1.0\", \"0.9595886982944456\", \"0.8456794214576862\"], \"2\": [\"0.7301587462425232\", \"0.6776819927198064\", \"0.7422882023613603\", \"0.6405926551643425\", \"0.5416789763424679\"], \"3\": [\"0.8288288116455078\", \"0.8946489212523333\", \"0.8568053182924886\", \"1.0\", \"1.0\"], \"4\": [\"0.9304388761520386\", \"1.0\", \"1.0\", \"0.9770146350190764\", \"1.0\"], \"5\": [\"0.9272727370262146\", \"0.8185682862892766\", \"0.7343316812356244\", \"0.5741541243871904\", \"0.44604741468587494\"], \"6\": [\"1.0\", \"1.0\", \"1.0\", \"0.9409442634495603\", \"0.9567972808983531\"], \"7\": [\"0.875\", \"0.9337626822366054\", \"0.9114279365305047\", \"1.0\", \"0.9420995692860883\"], \"8\": [\"0.9333333373069763\", \"0.8390371680917077\", \"0.8797118220534562\", \"0.8576076266752097\", \"0.8353335312464731\"], \"9\": [\"0.9748641848564148\", \"0.9793806822211983\", \"0.8747198096182981\", \"0.8417577383377725\", \"0.7924438732183218\"], \"10\": [\"0.8938053250312805\", \"0.945187396641226\", \"1.0\", \"1.0\", \"1.0\"], \"11\": [\"0.9852941036224365\", \"0.940773544565984\", \"1.0\", \"1.0\", \"0.9744433197081129\"], \"12\": [\"0.8589743375778198\", \"0.8865610194071949\", \"0.7586891415039964\", \"0.8971624873325016\", \"1.0\"], \"13\": [\"0.9178082346916199\", \"0.8598572506782375\", \"0.7585091748037436\", \"0.5311631320652456\", \"0.4961215285760771\"], \"14\": [\"0.9622710347175598\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.8938053250312805\", \"1.0\", \"0.8443982220470327\", \"0.8958046283624884\", \"0.8927867880071723\"], \"16\": [\"0.9661017060279846\", \"1.0\", \"0.9323038228091215\", \"1.0\", \"1.0\"], \"17\": [\"0.8507462739944458\", \"1.0\", \"1.0\", \"0.9549682369985835\", \"0.8843749365106865\"], \"18\": [\"0.9047619104385376\", \"0.7023552195467943\", \"0.6658228332690145\", \"0.6790478087914034\", \"0.6508387841142056\"], \"19\": [\"0.962199866771698\", \"1.0\", \"1.0\", \"0.9543924421085003\", \"0.8552515794947849\"], \"20\": [\"0.8938053250312805\", \"0.8894592623262126\", \"0.9876749049248656\", \"1.0\", \"1.0\"], \"21\": [\"0.9763779640197754\", \"0.9845057836238998\", \"0.8378172300427879\", \"0.773924095158208\", \"0.7827441687349376\"], \"22\": [\"0.8551723957061768\", \"0.7536699140630719\", \"0.7850306630873699\", \"0.8913712982873504\", \"0.8401728944711612\"], \"23\": [\"0.9117646813392639\", \"0.9976326350998985\", \"1.0\", \"0.9791517388696462\", \"0.86319274387944\"], \"24\": [\"0.9621540904045105\", \"1.0\", \"1.0\", \"1.0\", \"0.9373681933529148\"]}");
   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\": true, \"7\": false, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": false, \"13\": true, \"14\": true, \"15\": false, \"16\": true, \"17\": true, \"18\": false, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}");
+  var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": false, \"3\": true, \"4\": true, \"5\": false, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": false, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": false, \"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\"]}");
 
@@ -3024,7 +3024,7 @@ <h3>Ethical Considerations</h3>
   ];
 
   // create global variable for max_n_evals
-  var histories = JSON.parse("{\"0\": [\"0.8333333134651184\", \"0.8094662738308168\", \"0.8595458420662222\", \"0.9296908013627551\", \"1.0\"], \"1\": [\"0.9375\", \"0.9024813497004001\", \"0.8455642329624111\", \"0.7874311554591716\", \"0.8345889279760974\"], \"2\": [\"0.725806474685669\", \"0.8907857631730102\", \"0.776170444479959\", \"0.6162736849512381\", \"0.5839857016862101\"], \"3\": [\"0.8181818127632141\", \"0.8391178204004882\", \"0.8189110064644611\", \"0.7800686190826583\", \"0.7491002482285714\"], \"4\": [\"0.92046719789505\", \"0.9380071097762551\", \"0.947841009833929\", \"0.8667618645131204\", \"0.7170302376680323\"], \"5\": [\"0.942307710647583\", \"0.9694386258446315\", \"0.951919388633946\", \"0.9587879289048926\", \"1.0\"], \"6\": [\"0.970588207244873\", \"1.0\", \"0.9557762167388503\", \"0.8805875340586622\", \"0.747940294459579\"], \"7\": [\"0.9428571462631226\", \"0.8783779245235137\", \"0.8261186611612624\", \"0.7643053688953335\", \"0.5512282749864735\"], \"8\": [\"0.95652174949646\", \"0.8192877019173987\", \"0.8426576551838371\", \"0.9074657468620512\", \"0.9592384385038815\"], \"9\": [\"0.9529411792755127\", \"0.8798741723624461\", \"0.985830182978628\", \"0.9701480666125963\", \"0.9273903866439045\"], \"10\": [\"0.9359999895095825\", \"0.762792327372465\", \"0.7281058084401141\", \"0.831165238383092\", \"0.7764860309058591\"], \"11\": [\"1.0\", \"0.9850487289923939\", \"0.9518471498765199\", \"0.9670909080594012\", \"0.8751115995960776\"], \"12\": [\"0.9080459475517273\", \"0.8065706207296807\", \"0.6420965804007145\", \"0.6565092564864992\", \"0.47373432709702756\"], \"13\": [\"0.9518072009086609\", \"1.0\", \"1.0\", \"0.9824250927878341\", \"1.0\"], \"14\": [\"0.9838172793388367\", \"1.0\", \"0.8162290679117923\", \"0.7423937230911423\", \"0.7490988849771455\"], \"15\": [\"0.8217821717262268\", \"0.7287226371022525\", \"0.7150795125565077\", \"0.5491627294294421\", \"0.4008606031241557\"], \"16\": [\"0.8999999761581421\", \"0.9338681042203166\", \"0.9317241579241186\", \"1.0\", \"0.9078432043840289\"], \"17\": [\"0.7758620977401733\", \"0.8123644285359083\", \"0.7346746694626879\", \"0.7444220062324121\", \"0.8046637991320977\"], \"18\": [\"0.8333333134651184\", \"0.9964887386981487\", \"1.0\", \"0.9019569094861815\", \"0.6958002163405863\"], \"19\": [\"0.9109863638877869\", \"0.9246858504657817\", \"0.9015461561107054\", \"1.0\", \"1.0\"], \"20\": [\"0.8849557638168335\", \"0.9869658878824109\", \"1.0\", \"1.0\", \"1.0\"], \"21\": [\"0.961240291595459\", \"0.9841299232764547\", \"0.8977064505958645\", \"0.933054387411553\", \"0.8860241272457073\"], \"22\": [\"0.8551723957061768\", \"0.903950970086637\", \"1.0\", \"1.0\", \"0.9407837714504604\"], \"23\": [\"0.9051094651222229\", \"0.8992678516864064\", \"1.0\", \"0.8821243872887108\", \"0.8297785626156888\"], \"24\": [\"0.9511281847953796\", \"0.8302767893991136\", \"0.9074862305526562\", \"0.9049898951773042\", \"0.8820971497325474\"]}");
+  var histories = JSON.parse("{\"0\": [\"0.8333333134651184\", \"0.8587494487475286\", \"0.8257740602552828\", \"0.7028458044560064\", \"0.719231415204748\"], \"1\": [\"0.9583333134651184\", \"1.0\", \"1.0\", \"0.9595886982944456\", \"0.8456794214576862\"], \"2\": [\"0.7301587462425232\", \"0.6776819927198064\", \"0.7422882023613603\", \"0.6405926551643425\", \"0.5416789763424679\"], \"3\": [\"0.8288288116455078\", \"0.8946489212523333\", \"0.8568053182924886\", \"1.0\", \"1.0\"], \"4\": [\"0.9304388761520386\", \"1.0\", \"1.0\", \"0.9770146350190764\", \"1.0\"], \"5\": [\"0.9272727370262146\", \"0.8185682862892766\", \"0.7343316812356244\", \"0.5741541243871904\", \"0.44604741468587494\"], \"6\": [\"1.0\", \"1.0\", \"1.0\", \"0.9409442634495603\", \"0.9567972808983531\"], \"7\": [\"0.875\", \"0.9337626822366054\", \"0.9114279365305047\", \"1.0\", \"0.9420995692860883\"], \"8\": [\"0.9333333373069763\", \"0.8390371680917077\", \"0.8797118220534562\", \"0.8576076266752097\", \"0.8353335312464731\"], \"9\": [\"0.9748641848564148\", \"0.9793806822211983\", \"0.8747198096182981\", \"0.8417577383377725\", \"0.7924438732183218\"], \"10\": [\"0.8938053250312805\", \"0.945187396641226\", \"1.0\", \"1.0\", \"1.0\"], \"11\": [\"0.9852941036224365\", \"0.940773544565984\", \"1.0\", \"1.0\", \"0.9744433197081129\"], \"12\": [\"0.8589743375778198\", \"0.8865610194071949\", \"0.7586891415039964\", \"0.8971624873325016\", \"1.0\"], \"13\": [\"0.9178082346916199\", \"0.8598572506782375\", \"0.7585091748037436\", \"0.5311631320652456\", \"0.4961215285760771\"], \"14\": [\"0.9622710347175598\", \"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"15\": [\"0.8938053250312805\", \"1.0\", \"0.8443982220470327\", \"0.8958046283624884\", \"0.8927867880071723\"], \"16\": [\"0.9661017060279846\", \"1.0\", \"0.9323038228091215\", \"1.0\", \"1.0\"], \"17\": [\"0.8507462739944458\", \"1.0\", \"1.0\", \"0.9549682369985835\", \"0.8843749365106865\"], \"18\": [\"0.9047619104385376\", \"0.7023552195467943\", \"0.6658228332690145\", \"0.6790478087914034\", \"0.6508387841142056\"], \"19\": [\"0.962199866771698\", \"1.0\", \"1.0\", \"0.9543924421085003\", \"0.8552515794947849\"], \"20\": [\"0.8938053250312805\", \"0.8894592623262126\", \"0.9876749049248656\", \"1.0\", \"1.0\"], \"21\": [\"0.9763779640197754\", \"0.9845057836238998\", \"0.8378172300427879\", \"0.773924095158208\", \"0.7827441687349376\"], \"22\": [\"0.8551723957061768\", \"0.7536699140630719\", \"0.7850306630873699\", \"0.8913712982873504\", \"0.8401728944711612\"], \"23\": [\"0.9117646813392639\", \"0.9976326350998985\", \"1.0\", \"0.9791517388696462\", \"0.86319274387944\"], \"24\": [\"0.9621540904045105\", \"1.0\", \"1.0\", \"1.0\", \"0.9373681933529148\"]}");
   // 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 02ad78e49..902133afc 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>
-2024-06-04 00:53:06,964 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Database setup, ready to run queries!
+2024-06-10 20:24:33,098 <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>
-2024-06-04 00:53:18,179 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-10 20:25:03,567 <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>
-2024-06-04 00:53:18,181 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 10.109420 s
+2024-06-10 20:25:03,572 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 29.232611 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>
-2024-06-04 00:53:23,179 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-10 20:25:08,715 <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>
-2024-06-04 00:53:23,187 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 5.003255 s
+2024-06-10 20:25:08,716 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 5.142136 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>
-2024-06-04 00:53:27,354 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-10 20:25:15,429 <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>
-2024-06-04 00:53:27,356 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 0.379532 s
+2024-06-10 20:25:15,436 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 2.655993 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>
-2024-06-04 00:53:27,775 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-10 20:25:16,849 <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>
-2024-06-04 00:53:27,777 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 0.414571 s
+2024-06-10 20:25:16,851 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 1.408266 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>
-2024-06-04 00:53:27,959 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.orm    - Query returned successfully!
+2024-06-10 20:25:17,054 <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>
-2024-06-04 00:53:27,961 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 0.183215 s
+2024-06-10 20:25:17,056 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cycquery.utils.profile - Finished executing function run_query in 0.200159 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">
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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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-2024-06-04 00:53:53,759 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best learning_rate: 0.1
+2024-06-10 20:28:00,853 <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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-2024-06-04 00:53:53,760 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 10
+2024-06-10 20:28:00,855 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best gamma: 1
 </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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-2024-06-04 00:53:53,761 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 1
+2024-06-10 20:28:00,856 <span class="ansi-white-intense-fg ansi-bold">INFO</span> cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.7
 </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=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=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=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=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=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=2,
+              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=0.7, 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=2,
+              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>
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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;: 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;: 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;: 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;: 2, &#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}
 </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": "4bca96d72aec4ee3b2869183ae7a325b"}</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": "9bbb892039184705b55f740b96a65ed1"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "95d19aaa530344539bbdd9d34c922ba1"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "69fd7bf8b7a7431e9faf6177c67c567d"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "a313611fcb374028a1bfa19420d752b9"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "2b445dc84efd43bcbb586cfd1a34ed7c"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "ecd42c7fb4f241c98c0f14f0ba4b5ee8"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "87ee6ee8e1944c9085479208ddac7197"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "88cdb4657fbe4fe3943df7a048d8a1b5"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "92311e1b260a4668bd81491cf7d97263"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "74df91d9202142db8dbbe59002ec0a6c"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "5a1d93904b0f48699d4e28fa964bdd8f"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "ba0b873d7f6d4bde94c6036e8718de5a"}</script></div>
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+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "c83c8a0ae08a4eb1b220e3bcfca0ce15"}</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 9d16ea1ea..1d2ddd812 100644
--- a/api/tutorials/synthea/los_prediction.ipynb
+++ b/api/tutorials/synthea/los_prediction.ipynb
@@ -33,10 +33,10 @@
    "id": "53009e6b",
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-     "iopub.execute_input": "2024-06-04T04:52:50.894483Z",
-     "iopub.status.busy": "2024-06-04T04:52:50.893205Z",
-     "iopub.status.idle": "2024-06-04T04:53:05.257217Z",
-     "shell.execute_reply": "2024-06-04T04:53:05.255086Z"
+     "iopub.execute_input": "2024-06-11T00:24:15.751897Z",
+     "iopub.status.busy": "2024-06-11T00:24:15.751337Z",
+     "iopub.status.idle": "2024-06-11T00:24:31.182594Z",
+     "shell.execute_reply": "2024-06-11T00:24:31.180573Z"
     }
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@@ -97,10 +97,10 @@
    "id": "afae58a8-5708-4e05-8695-25ba3ce1a71f",
    "metadata": {
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-     "iopub.execute_input": "2024-06-04T04:53:05.266010Z",
-     "iopub.status.busy": "2024-06-04T04:53:05.264727Z",
-     "iopub.status.idle": "2024-06-04T04:53:05.273413Z",
-     "shell.execute_reply": "2024-06-04T04:53:05.271682Z"
+     "iopub.execute_input": "2024-06-11T00:24:31.194141Z",
+     "iopub.status.busy": "2024-06-11T00:24:31.191745Z",
+     "iopub.status.idle": "2024-06-11T00:24:31.200737Z",
+     "shell.execute_reply": "2024-06-11T00:24:31.199273Z"
     },
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@@ -123,10 +123,10 @@
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-     "iopub.execute_input": "2024-06-04T04:53:05.280537Z",
-     "iopub.status.busy": "2024-06-04T04:53:05.279708Z",
-     "iopub.status.idle": "2024-06-04T04:53:05.287833Z",
-     "shell.execute_reply": "2024-06-04T04:53:05.286110Z"
+     "iopub.execute_input": "2024-06-11T00:24:31.208061Z",
+     "iopub.status.busy": "2024-06-11T00:24:31.207209Z",
+     "iopub.status.idle": "2024-06-11T00:24:31.216439Z",
+     "shell.execute_reply": "2024-06-11T00:24:31.214606Z"
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@@ -157,10 +157,10 @@
    "id": "e497df9f-0f3d-4e9c-845c-539627a37f67",
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-     "iopub.execute_input": "2024-06-04T04:53:05.295571Z",
-     "iopub.status.busy": "2024-06-04T04:53:05.294493Z",
-     "iopub.status.idle": "2024-06-04T04:53:28.038741Z",
-     "shell.execute_reply": "2024-06-04T04:53:28.036502Z"
+     "iopub.execute_input": "2024-06-11T00:24:31.239411Z",
+     "iopub.status.busy": "2024-06-11T00:24:31.238692Z",
+     "iopub.status.idle": "2024-06-11T00:25:17.136085Z",
+     "shell.execute_reply": "2024-06-11T00:25:17.134287Z"
     },
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@@ -169,77 +169,77 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:06,964 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Database setup, ready to run queries!\n"
+      "2024-06-10 20:24:33,098 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Database setup, ready to run queries!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:18,179 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
+      "2024-06-10 20:25:03,567 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:18,181 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 10.109420 s\n"
+      "2024-06-10 20:25:03,572 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 29.232611 s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:23,179 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
+      "2024-06-10 20:25:08,715 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:23,187 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 5.003255 s\n"
+      "2024-06-10 20:25:08,716 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 5.142136 s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:27,354 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
+      "2024-06-10 20:25:15,429 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:27,356 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.379532 s\n"
+      "2024-06-10 20:25:15,436 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 2.655993 s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:27,775 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
+      "2024-06-10 20:25:16,849 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:27,777 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.414571 s\n"
+      "2024-06-10 20:25:16,851 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 1.408266 s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:27,959 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
+      "2024-06-10 20:25:17,054 \u001b[1;37mINFO\u001b[0m cycquery.orm    - Query returned successfully!\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:27,961 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.183215 s\n"
+      "2024-06-10 20:25:17,056 \u001b[1;37mINFO\u001b[0m cycquery.utils.profile - Finished executing function run_query in 0.200159 s\n"
      ]
     }
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@@ -398,10 +398,10 @@
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@@ -1579,10 +1579,10 @@
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@@ -1606,10 +1606,10 @@
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-     "shell.execute_reply": "2024-06-04T04:53:28.404347Z"
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@@ -1641,8 +1641,8 @@
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@@ -2500,9 +2500,9 @@
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@@ -2559,10 +2559,10 @@
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@@ -2588,10 +2588,10 @@
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@@ -8838,10 +8838,10 @@
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-     "shell.execute_reply": "2024-06-04T04:53:29.318803Z"
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@@ -8870,10 +8870,10 @@
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-     "iopub.status.idle": "2024-06-04T04:53:29.560239Z",
-     "shell.execute_reply": "2024-06-04T04:53:29.558516Z"
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@@ -8882,7 +8882,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'age': 'numeric', 'gender': 'binary', 'obs_Calcium [Mass/volume] in Serum or Plasma': 'numeric', 'outcome': 'binary', 'obs_Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Chloride [Moles/volume] in Serum or Plasma': 'numeric', 'obs_MCHC [Mass/volume] by Automated count': 'numeric', 'obs_Albumin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Urea nitrogen [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Hematocrit [Volume Fraction] of Blood by Automated count': 'numeric', 'obs_Hemoglobin [Mass/volume] in Blood': 'numeric', 'obs_Glucose [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Protein [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Leukocytes [#/volume] in Blood by Automated count': 'numeric', 'obs_Body Weight': 'numeric', 'obs_Erythrocytes [#/volume] in Blood by Automated count': 'numeric', 'n_procedures': 'numeric', 'obs_Potassium [Moles/volume] in Serum or Plasma': 'numeric', 'n_obs': 'numeric', 'obs_Bilirubin.total [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Ferritin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Systolic Blood Pressure': 'numeric', 'obs_Platelets [#/volume] in Blood by Automated count': 'numeric', 'obs_Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Oxygen saturation in Arterial blood': 'numeric', 'obs_Carbon dioxide  total [Moles/volume] in Serum or Plasma': 'numeric', 'obs_MCV [Entitic volume] by Automated count': 'numeric', 'obs_MCH [Entitic mass] by Automated count': 'numeric', 'obs_Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Creatinine [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Sodium [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Diastolic Blood Pressure': 'numeric', 'obs_Glomerular filtration rate/1.73 sq M.predicted': 'numeric', 'n_meds': 'ordinal', 'obs_Erythrocyte distribution width [Ratio] by Automated count': 'numeric', 'obs_Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method': 'numeric'}\n"
+      "{'obs_Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Bilirubin.total [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Systolic Blood Pressure': 'numeric', 'obs_Oxygen saturation in Arterial blood': 'numeric', 'obs_MCHC [Mass/volume] by Automated count': 'numeric', 'obs_Carbon dioxide  total [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Body Weight': 'numeric', 'obs_Glomerular filtration rate/1.73 sq M.predicted': 'numeric', 'obs_Albumin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Sodium [Moles/volume] in Serum or Plasma': 'numeric', 'obs_MCV [Entitic volume] by Automated count': 'numeric', 'obs_Urea nitrogen [Mass/volume] in Serum or Plasma': 'numeric', 'gender': 'binary', 'obs_Ferritin [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method': 'numeric', 'outcome': 'binary', 'obs_Calcium [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Creatinine [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Erythrocytes [#/volume] in Blood by Automated count': 'numeric', 'obs_Erythrocyte distribution width [Ratio] by Automated count': 'numeric', 'obs_MCH [Entitic mass] by Automated count': 'numeric', 'age': 'numeric', 'n_obs': 'numeric', 'obs_Glucose [Mass/volume] in Serum or Plasma': 'numeric', 'obs_Hematocrit [Volume Fraction] of Blood by Automated count': 'numeric', 'obs_Potassium [Moles/volume] in Serum or Plasma': 'numeric', 'obs_Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma': 'numeric', 'obs_Diastolic Blood Pressure': 'numeric', 'obs_Chloride [Moles/volume] in Serum or Plasma': 'numeric', 'n_meds': 'ordinal', 'obs_Leukocytes [#/volume] in Blood by Automated count': 'numeric', 'obs_Platelets [#/volume] in Blood by Automated count': 'numeric', 'n_procedures': 'numeric', 'obs_Hemoglobin [Mass/volume] in Blood': 'numeric', 'obs_Protein [Mass/volume] in Serum or Plasma': 'numeric'}\n"
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@@ -8950,10 +8950,10 @@
    "id": "2bb80cfb-2e48-4ecf-a325-0026dad4aef7",
    "metadata": {
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-     "iopub.execute_input": "2024-06-04T04:53:29.567552Z",
-     "iopub.status.busy": "2024-06-04T04:53:29.566215Z",
-     "iopub.status.idle": "2024-06-04T04:53:29.575504Z",
-     "shell.execute_reply": "2024-06-04T04:53:29.573904Z"
+     "iopub.execute_input": "2024-06-11T00:25:18.905573Z",
+     "iopub.status.busy": "2024-06-11T00:25:18.904220Z",
+     "iopub.status.idle": "2024-06-11T00:25:18.916842Z",
+     "shell.execute_reply": "2024-06-11T00:25:18.915152Z"
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@@ -8974,10 +8974,10 @@
    "id": "31ec3441-7dba-4b9a-9256-ad6b0293829d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:29.581839Z",
-     "iopub.status.busy": "2024-06-04T04:53:29.580889Z",
-     "iopub.status.idle": "2024-06-04T04:53:29.687172Z",
-     "shell.execute_reply": "2024-06-04T04:53:29.685521Z"
+     "iopub.execute_input": "2024-06-11T00:25:18.930595Z",
+     "iopub.status.busy": "2024-06-11T00:25:18.927560Z",
+     "iopub.status.idle": "2024-06-11T00:25:19.045966Z",
+     "shell.execute_reply": "2024-06-11T00:25:19.044040Z"
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@@ -9004,10 +9004,10 @@
    "id": "f2fb5345-17f9-44ac-b944-079324a098e4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:29.693657Z",
-     "iopub.status.busy": "2024-06-04T04:53:29.692665Z",
-     "iopub.status.idle": "2024-06-04T04:53:29.706465Z",
-     "shell.execute_reply": "2024-06-04T04:53:29.704800Z"
+     "iopub.execute_input": "2024-06-11T00:25:19.054194Z",
+     "iopub.status.busy": "2024-06-11T00:25:19.052690Z",
+     "iopub.status.idle": "2024-06-11T00:25:19.066914Z",
+     "shell.execute_reply": "2024-06-11T00:25:19.065410Z"
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@@ -9035,10 +9035,10 @@
    "id": "868fa57c-f12a-4821-a85e-31c6c95d6e76",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:29.712870Z",
-     "iopub.status.busy": "2024-06-04T04:53:29.711895Z",
-     "iopub.status.idle": "2024-06-04T04:53:29.720435Z",
-     "shell.execute_reply": "2024-06-04T04:53:29.718833Z"
+     "iopub.execute_input": "2024-06-11T00:25:19.073769Z",
+     "iopub.status.busy": "2024-06-11T00:25:19.073132Z",
+     "iopub.status.idle": "2024-06-11T00:25:19.088404Z",
+     "shell.execute_reply": "2024-06-11T00:25:19.086296Z"
     },
     "tags": []
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@@ -9069,10 +9069,10 @@
    "id": "87b72a66-fc46-4e7a-a612-1f66de811c0d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:29.727054Z",
-     "iopub.status.busy": "2024-06-04T04:53:29.726091Z",
-     "iopub.status.idle": "2024-06-04T04:53:29.877523Z",
-     "shell.execute_reply": "2024-06-04T04:53:29.875872Z"
+     "iopub.execute_input": "2024-06-11T00:25:19.096200Z",
+     "iopub.status.busy": "2024-06-11T00:25:19.095347Z",
+     "iopub.status.idle": "2024-06-11T00:25:19.251800Z",
+     "shell.execute_reply": "2024-06-11T00:25:19.250568Z"
     },
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@@ -9100,10 +9100,10 @@
    "id": "c6370e7a-c559-431f-9ebc-0b35bcdfb029",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:29.883861Z",
-     "iopub.status.busy": "2024-06-04T04:53:29.883264Z",
-     "iopub.status.idle": "2024-06-04T04:53:30.026321Z",
-     "shell.execute_reply": "2024-06-04T04:53:30.024613Z"
+     "iopub.execute_input": "2024-06-11T00:25:19.259092Z",
+     "iopub.status.busy": "2024-06-11T00:25:19.258325Z",
+     "iopub.status.idle": "2024-06-11T00:25:19.419923Z",
+     "shell.execute_reply": "2024-06-11T00:25:19.418123Z"
     },
     "tags": []
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@@ -9111,7 +9111,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "84d17af6c12e4be3a792370edaa3931d",
+       "model_id": "db80a35957434143a8ce07cf0872a54a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -9148,10 +9148,10 @@
    "id": "42e8d147-545c-47a7-9a91-b06b27a37d47",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:30.033466Z",
-     "iopub.status.busy": "2024-06-04T04:53:30.032134Z",
-     "iopub.status.idle": "2024-06-04T04:53:33.080841Z",
-     "shell.execute_reply": "2024-06-04T04:53:33.079038Z"
+     "iopub.execute_input": "2024-06-11T00:25:19.427711Z",
+     "iopub.status.busy": "2024-06-11T00:25:19.426729Z",
+     "iopub.status.idle": "2024-06-11T00:25:22.617339Z",
+     "shell.execute_reply": "2024-06-11T00:25:22.615651Z"
     },
     "tags": []
    },
@@ -9177,10 +9177,10 @@
    "id": "01d36851-292a-4849-98e0-9183c2dec87a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:33.089267Z",
-     "iopub.status.busy": "2024-06-04T04:53:33.087823Z",
-     "iopub.status.idle": "2024-06-04T04:53:33.100574Z",
-     "shell.execute_reply": "2024-06-04T04:53:33.098789Z"
+     "iopub.execute_input": "2024-06-11T00:25:22.626592Z",
+     "iopub.status.busy": "2024-06-11T00:25:22.625464Z",
+     "iopub.status.idle": "2024-06-11T00:25:22.648538Z",
+     "shell.execute_reply": "2024-06-11T00:25:22.646835Z"
     },
     "tags": []
    },
@@ -9223,10 +9223,10 @@
    "id": "572b0fe0-5891-4aca-9e63-8d556851caec",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:33.108524Z",
-     "iopub.status.busy": "2024-06-04T04:53:33.107174Z",
-     "iopub.status.idle": "2024-06-04T04:53:53.786291Z",
-     "shell.execute_reply": "2024-06-04T04:53:53.784408Z"
+     "iopub.execute_input": "2024-06-11T00:25:22.659805Z",
+     "iopub.status.busy": "2024-06-11T00:25:22.658222Z",
+     "iopub.status.idle": "2024-06-11T00:28:00.882612Z",
+     "shell.execute_reply": "2024-06-11T00:28:00.880665Z"
     },
     "tags": []
    },
@@ -9235,42 +9235,42 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:53,730 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 10\n"
+      "2024-06-10 20:28:00,843 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best reg_lambda: 1\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:53,756 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 250\n"
+      "2024-06-10 20:28:00,850 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best n_estimators: 100\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:53,757 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 5\n"
+      "2024-06-10 20:28:00,852 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best max_depth: 2\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:53,759 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
+      "2024-06-10 20:28:00,853 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best learning_rate: 0.1\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:53,760 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 10\n"
+      "2024-06-10 20:28:00,855 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best gamma: 1\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 00:53:53,761 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 1\n"
+      "2024-06-10 20:28:00,856 \u001b[1;37mINFO\u001b[0m cyclops.models.wrappers.sk_model - Best colsample_bytree: 0.7\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=10, 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=10, 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=1, 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=2,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=100, 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=1, 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=2,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=100, 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=10, 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=1, 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=2,\n",
+       "              max_leaves=None, min_child_weight=3, missing=nan,\n",
+       "              monotone_constraints=None, n_estimators=100, 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": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:53.811151Z",
-     "iopub.status.busy": "2024-06-04T04:53:53.810072Z",
-     "iopub.status.idle": "2024-06-04T04:53:53.820156Z",
-     "shell.execute_reply": "2024-06-04T04:53:53.818385Z"
+     "iopub.execute_input": "2024-06-11T00:28:00.891765Z",
+     "iopub.status.busy": "2024-06-11T00:28:00.890293Z",
+     "iopub.status.idle": "2024-06-11T00:28:00.900975Z",
+     "shell.execute_reply": "2024-06-11T00:28:00.899230Z"
     },
     "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': 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': 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': 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': 2, '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"
      ]
     }
    ],
@@ -9782,10 +9782,10 @@
    "id": "4cfdfe29-c813-45cd-9ae7-2959daa0cd8f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:53.826947Z",
-     "iopub.status.busy": "2024-06-04T04:53:53.826122Z",
-     "iopub.status.idle": "2024-06-04T04:53:53.835162Z",
-     "shell.execute_reply": "2024-06-04T04:53:53.833407Z"
+     "iopub.execute_input": "2024-06-11T00:28:00.908030Z",
+     "iopub.status.busy": "2024-06-11T00:28:00.907049Z",
+     "iopub.status.idle": "2024-06-11T00:28:00.916575Z",
+     "shell.execute_reply": "2024-06-11T00:28:00.914862Z"
     }
    },
    "outputs": [],
@@ -9809,10 +9809,10 @@
    "id": "c3fee35c-902e-4c54-89b7-c42c1e935e1b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T04:53:53.842119Z",
-     "iopub.status.busy": "2024-06-04T04:53:53.841115Z",
-     "iopub.status.idle": "2024-06-04T04:53:54.139554Z",
-     "shell.execute_reply": "2024-06-04T04:53:54.137816Z"
+     "iopub.execute_input": "2024-06-11T00:28:00.923583Z",
+     "iopub.status.busy": "2024-06-11T00:28:00.922612Z",
+     "iopub.status.idle": "2024-06-11T00:28:01.241151Z",
+     "shell.execute_reply": "2024-06-11T00:28:01.239434Z"
     },
     "tags": []
    },
@@ -9820,7 +9820,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "856ef701d2d84079909703b74546c426",
+       "model_id": "4bca96d72aec4ee3b2869183ae7a325b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -9868,10 +9868,10 @@
    "id": "d3d5b101-17a8-4610-ae17-e04cf962d2d1",
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@@ -12467,10 +13081,10 @@
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-     "iopub.status.idle": "2024-06-04T04:53:56.336725Z",
-     "shell.execute_reply": "2024-06-04T04:53:56.335136Z"
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+     "iopub.status.busy": "2024-06-11T00:28:03.606845Z",
+     "iopub.status.idle": "2024-06-11T00:28:03.615736Z",
+     "shell.execute_reply": "2024-06-11T00:28:03.614147Z"
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@@ -12490,10 +13104,10 @@
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-     "iopub.status.busy": "2024-06-04T04:53:56.342583Z",
-     "iopub.status.idle": "2024-06-04T04:53:56.399387Z",
-     "shell.execute_reply": "2024-06-04T04:53:56.397751Z"
+     "iopub.execute_input": "2024-06-11T00:28:03.622683Z",
+     "iopub.status.busy": "2024-06-11T00:28:03.621632Z",
+     "iopub.status.idle": "2024-06-11T00:28:03.677495Z",
+     "shell.execute_reply": "2024-06-11T00:28:03.675876Z"
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    "outputs": [
@@ -12514,11 +13128,11 @@
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-          0.961240291595459,
+          0.8938053250312805,
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           0.8551723957061768,
-          0.9051094651222229,
-          0.9511281847953796
+          0.9117646813392639,
+          0.9621540904045105
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        ],
@@ -13355,9 +13969,9 @@
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@@ -13406,10 +14020,10 @@
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@@ -13432,10 +14046,10 @@
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