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This article explains the SHAP (Shapley Additive Explanation) method, its theoretical foundations in game theory, the computation of Shapley Values, various algorithmic approximations like TreeSHAP and DeepSHAP, practical code examples, and the strengths and limitations of using SHAP for model interpretability.
This article explains how to interpret a HiMCM 2020 A‑problem model by creating diverse fictional personas, generating appropriate virtual data, analyzing results, and using effective visualizations such as radar, bar, and table charts to clearly communicate model insights.
This article summarizes the Ant Financial paper presented at DASFAA 2018 that proposes a universal local explanation method for Gradient Boosting Decision Tree models, detailing the problem definition, the PMML‑based algorithm for attributing feature contributions, experimental validation on fraud detection data, and the practical benefits for model transparency and improvement.