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User Guide
1. Introduction
2. Data Pipeline
3. Model Train and Tune
4. Post-hoc Explainability
5. Interpretable Models
6. Diagnostic Suite
6.1. Accuracy
6.2. WeakSpot
6.3. Overfit
6.4. Reliability
6.5. Robustness
6.6. Resilience
6.7. Fairness
6.8. Segmented
6.9. Scored Test
7. Model Comparison
8. Case Studies
6.
Diagnostic Suite
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6.1. Accuracy
6.1.1. Regression Tasks
6.1.2. Binary Classification
6.1.3. Examples
6.2. WeakSpot
6.2.1. Algorithm Details
6.2.2. Usage
6.2.3. Examples
6.3. Overfit
6.3.1. Algorithm Details
6.3.2. Usage
6.3.3. Examples
6.4. Reliability
6.4.1. Reliability for Regression Tasks
6.4.2. Reliability for Binary Classification
6.4.3. Examples
6.5. Robustness
6.5.1. Algorithm Details
6.5.2. Usage
6.5.3. Examples
6.6. Resilience
6.6.1. Algorithm Details
6.6.2. Usage
6.6.3. Examples
6.7. Fairness
6.7.1. Fairness Metrics
6.7.2. Fairness Segmented
6.7.3. Fairness Binning
6.7.4. Fairness Thresholding
6.7.5. Examples
6.8. Segmented
6.8.1. Methodology
6.8.2. Usage
6.8.3. Examples
6.9. Scored Test
6.9.1. Usage
6.9.2. Examples
