SHAP Values for Model Explainability
shap.Explainer, TreeExplainer, force plots, beeswarm plots, feature importance ranking.
Why Explainability
Complex models are accurate but opaque. Explainability answers "why did the model make this prediction?", which is essential for trust, debugging, and regulatory compliance. SHAP is one of the most widely used explanation methods.
What SHAP Measures
SHAP (SHapley Additive exPlanations) assigns each feature a contribution to a single prediction. The values are based on Shapley values from cooperative game theory: each feature is a "player" and its SHAP value is its fair share of the prediction relative to a baseline.
All lessons in this course
- Bias Detection in ML Models
- SHAP Values for Model Explainability
- LIME: Local Interpretable Explanations
- AI Ethics and Governance Frameworks