Exploring SHAP for model interpretation
To understand what SHapley Additive exPlanations (SHAP) is and how it works, imagine that you’re playing a team sport, and each player contributes to the team’s overall score. Measuring how each team member impacts the score is similar to how SHAP works. SHAP quantifies each feature’s contribution to a model’s prediction. Based on game theory’s Shapley values, SHAP provides a consistent and mathematically sound method for explaining both global model behavior and individual predictions.
Why SHAP is useful
SHAP is valuable because it offers consistent, interpretable insights into model behavior. It ensures that if a feature is more influential in one model than in another, it receives a higher contribution score. This approach allows SHAP to give both local and global explanations for feature importance in a model.
Local explanations, also called local importance, help us understand how each feature...
