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Debiasing SHAP scores in random forests

Author

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  • Markus Loecher

    (Berlin School of Economics and Law)

Abstract

Black box machine learning models are currently being used for high-stakes decision making in various parts of society such as healthcare and criminal justice. While tree-based ensemble methods such as random forests typically outperform deep learning models on tabular data sets, their built-in variable importance algorithms are known to be strongly biased toward high-entropy features. It was recently shown that the increasingly popular SHAP (SHapley Additive exPlanations) values suffer from a similar bias. We propose debiased or "shrunk" SHAP scores based on sample splitting which additionally enable the detection of overfitting issues at the feature level.

Suggested Citation

  • Markus Loecher, 2024. "Debiasing SHAP scores in random forests," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(2), pages 427-440, June.
    • Handle: RePEc:spr:alstar:v:108:y:2024:i:2:d:10.1007_s10182-023-00479-7
    DOI: 10.1007/s10182-023-00479-7
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