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User Guide
1. Introduction
2. Data Pipeline
3. Model Train and Tune
4. Post-hoc Explainability
4.1. PFI (Permutation Feature Importance)
4.2. PDP (Partial Dependence Plot)
4.3. Hstats (Friedman’s H-statistic)
4.4. ICE (Individual Conditional Expectation)
4.5. ALE (Accumulated Local Effects)
4.6. LIME (Local Interpretable Model-Agnostic Explanation)
4.7. SHAP (SHapley Additive exPlanations)
5. Interpretable Models
6. Diagnostic Suite
7. Model Comparison
8. Case Studies
4.
Post-hoc Explainability
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4.1. PFI (Permutation Feature Importance)
4.1.1. Algorithm Details
4.1.2. Usage
4.1.3. Example
4.2. PDP (Partial Dependence Plot)
4.2.1. Algorithm Details
4.2.2. Usage
4.2.3. Examples
4.3. Hstats (Friedman’s H-statistic)
4.3.1. Algorithm Details
4.3.2. Usage
4.3.3. Examples
4.4. ICE (Individual Conditional Expectation)
4.4.1. Algorithm Details
4.4.2. Usage
4.4.3. Examples
4.5. ALE (Accumulated Local Effects)
4.5.1. Algorithm Details
4.5.2. Usage
4.5.3. Examples
4.6. LIME (Local Interpretable Model-Agnostic Explanation)
4.6.1. Algorithm Details
4.6.2. Usage
4.6.3. Examples
4.7. SHAP (SHapley Additive exPlanations)
4.7.1. Algorithm Details
4.7.2. Usage
4.7.3. Examples
