Web Reference: Feb 25, 2026 · Learn to explain ML predictions with SHAP values in Python. Covers scikit-learn, XGBoost, LightGBM with code examples, visualizations, and production tips. SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations). Jul 14, 2025 · This example shows that SHAP can effectively interpret predictions from even simple models like decision trees, making it a tool for understanding both black-box and transparent models across a wide range of machine learning applications.
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