SHAP Values: Global and Local Feature Importance
Learners will compute SHAP values for a gradient-boosting model, plot beeswarm and bar summaries, and explain a single prediction to a non-technical stakeholder.
Why Model Explainability Matters
Explainability is the ability to understand why a model made a specific prediction. In high-stakes domains like lending, healthcare, and hiring, regulators and users demand explanations — not just accurate predictions. SHAP (SHapley Additive exPlanations) provides a mathematically principled framework rooted in cooperative game theory to deliver these explanations for any model.
Shapley Values: The Game Theory Origin
SHAP values borrow from Shapley values in cooperative game theory, where players (features) collaborate to produce an outcome (prediction). Each feature receives a fair share of credit by averaging its marginal contribution across all possible feature orderings. This makes SHAP the only additive attribution method satisfying the axioms of efficiency, symmetry, dummy, and additivity.
All lessons in this course
- SHAP Values: Global and Local Feature Importance
- LIME: Local Interpretable Model-Agnostic Explanations
- Fairness Metrics: Demographic Parity and Equal Opportunity
- Bias Mitigation Strategies: Pre-processing, In-processing, and Post-processing