Web Reference: Feb 25, 2026 · Learn how to use SHAP values to explain machine learning predictions in Python. Practical guide with scikit-learn, XGBoost, and LightGBM code examples, visualization techniques, and production-ready interpretability patterns. 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). 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).
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