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Explaining classifiers with measures of statistical association

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  • Borgonovo, Emanuele
  • Ghidini, Valentina
  • Hahn, Roman
  • Plischke, Elmar

Abstract

A new class of probabilistic sensitivity measures that quantifies the degree of association between covariates and generic targets used in classification is proposed, and it is shown that such class possesses the zero-independence property. Corresponding estimators are introduced, asymptotic consistency is proven and bootstrap is used to quantify uncertainty in the estimates. The use of the new dependence measures as explanations in a statistical machine learning context is illustrated. The resulting approach, called Xi-method, is demonstrated through applications involving different data formats: tabular, visual and textual.

Suggested Citation

  • Borgonovo, Emanuele & Ghidini, Valentina & Hahn, Roman & Plischke, Elmar, 2023. "Explaining classifiers with measures of statistical association," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:csdana:v:182:y:2023:i:c:s0167947323000129
    DOI: 10.1016/j.csda.2023.107701
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    References listed on IDEAS

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    1. Wenliang Pan & Xueqin Wang & Heping Zhang & Hongtu Zhu & Jin Zhu, 2020. "Ball Covariance: A Generic Measure of Dependence in Banach Space," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 307-317, January.
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    7. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    8. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    9. Dunson, David B., 2018. "Statistics in the big data era: Failures of the machine," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 4-9.
    10. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
    11. Qingyuan Zhao & Trevor Hastie, 2021. "Causal Interpretations of Black-Box Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 272-281, January.
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    Cited by:

    1. Silvana M. Pesenti & Pietro Millossovich & Andreas Tsanakas, 2023. "Differential Quantile-Based Sensitivity in Discontinuous Models," Papers 2310.06151, arXiv.org, revised Oct 2024.

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