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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
7. Model Comparison
7.1. Comparison for Regression
7.2. Comparison for Classification
7.3. Fairness Comparison
8. Case Studies
7.
Model Comparison
¶
Comparing different explanation methods and diagnostic results of multiple machine learning models.
7.1. Comparison for Regression
7.1.1. Accuracy Comparison
7.1.2. Overfit Comparison
7.1.3. Reliability Comparison
7.1.4. Robustness Comparison
7.1.5. Resilience Comparison
7.1.6. Examples
7.2. Comparison for Classification
7.2.1. Accuracy Comparison
7.2.2. Overfit Comparison
7.2.3. Reliability Comparison
7.2.4. Robustness Comparison
7.2.5. Resilience Comparison
7.2.6. Examples
7.3. Fairness Comparison
7.3.1. Fairness Metrics
7.3.2. Segmented
7.3.3. Examples