Model Interpretability
A blog series exploring practical tools for understanding, visualizing, and explaining model behavior.
1. PDPs: Partial Dependence Plots
An intuitive first step into understanding feature-level model behavior
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2. ICE Plots: Individual Conditional Expectations
From average effects to personalized curves—how ICE plots complement PDPs.
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3. SHAP Values: Shapley Additive Explanations
Game theory meets interpretability—understanding SHAP values and what they reveal.
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4. ALE Plots: Accumulated Local Effects
Robust global understanding for real-world datasets
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5. LIME: Local Interpretable Model-agnostic Explanations
Explaining any classifier prediction in an interpretable and faithful manner
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6. Counterfactual Explanations
Creating transparency and fairness through alternative paths to success
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