seniorSupervised Learning
What is SHAP and why is it used in supervised learning?
Updated May 17, 2026
Short answer
SHAP explains model predictions by assigning contribution values to each feature.
Deep explanation
SHAP (SHapley Additive exPlanations) is based on cooperative game theory. It assigns each feature a contribution value representing its impact on prediction. SHAP ensures consistency and local interpretability, making it widely used in explainable AI.
Real-world example
Explaining why a bank rejected a loan application.
Common mistakes
- Using SHAP without understanding baseline assumptions.
Follow-up questions
- What is the difference between SHAP and feature importance?
- Why is SHAP computationally expensive?