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SHAP for additively modeled features in a boosted trees model (2207.14490v1)
Published 29 Jul 2022 in stat.ML and cs.LG
Abstract: An important technique to explore a black-box ML model is called SHAP (SHapley Additive exPlanation). SHAP values decompose predictions into contributions of the features in a fair way. We will show that for a boosted trees model with some or all features being additively modeled, the SHAP dependence plot of such a feature corresponds to its partial dependence plot up to a vertical shift. We illustrate the result with XGBoost.
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