SHAP for additively modeled features in a boosted trees model

Michael Mayer

SHAP for additively modeled features in a boosted trees model

Machine Learning 2022-08-01 v1 Machine Learning

Authors: Michael Mayer

Abstract

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.

Keywords

machine learning

Cite

@article{arxiv.2207.14490,
  title  = {SHAP for additively modeled features in a boosted trees model},
  author = {Michael Mayer},
  journal= {arXiv preprint arXiv:2207.14490},
  year   = {2022}
}

Comments

15 pages, 5 figures

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