A Gentle Introduction to SHAP for Tree-Based Models

SHAP (SHapley Additive exPlanations) provides a principled approach to explain individual predictions from tree-based models like XGBoost, LightGBM, and Random Forest. Unlike traditional feature importance that shows general patterns, SHAP decomposes each prediction into individual feature contributions, revealing exactly how each feature influences specific outcomes. The TreeExplainer is optimized for tree-based models, computing exact SHAP values efficiently by leveraging tree structures. Through waterfall plots for individual predictions and summary plots for global insights, SHAP transforms black-box models into transparent systems that stakeholders can understand and trust.