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Shapley Values Infidelity

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  • Tom, Daniel M. Ph.D.

Abstract

Shapley values attempt to explain ML models using flat additive factors disregarding any tree hierarchy, and fails to distinguish between two different trees. We have been using log odds for segmentation tree of logistic regression models. Log odds faithfully reflect tree hierarchy and therefore explain decision trees, forests, and GBM much better.

Suggested Citation

  • Tom, Daniel M. Ph.D., 2023. "Shapley Values Infidelity," OSF Preprints 2p5d3, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:2p5d3
    DOI: 10.31219/osf.io/2p5d3
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    1. Charles B. Perkins & J. Christina Wang, 2019. "How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data," Working Papers 19-16, Federal Reserve Bank of Boston.
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