SHAP for additively modeled features in a boosted trees model
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Abstract (arXiv)
An important technique to explore a black-box machine learning (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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 20 references that are in the map. First posted on arXiv 29 Jul 2022.
Builds on
4 of 20 references.
References · 4
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 246
- 2019Explaining individual predictions when features are dependent: More accurate approximations to Shapley valuescited by 208
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Citing papers per year: 2023: 1.
1 paper.
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