Related papers: Statistical Significance of Feature Importance Rankings

Statistical Significance of Feature Importance Rankings

URL: http://arxiv.org/abs/2401.15800v4
Date: Thu, 30 Jan 2025 23:25:22 GMT
Title: Statistical Significance of Feature Importance Rankings
Authors: Jeremy Goldwasser, Giles Hooker,
Abstract summary: We devise techniques that ensure the most important features are correct with high-probability guarantees. These assess the set of $K$ top-ranked features, as well as the order of its elements. We then introduce two efficient sampling algorithms that identify the $K$ most important features, perhaps in order, with probability exceeding $1-alpha$.
Score: 3.8642937395065124
License:
Abstract: Feature importance scores are ubiquitous tools for understanding the predictions of machine learning models. However, many popular attribution methods suffer from high instability due to random sampling. Leveraging novel ideas from hypothesis testing, we devise techniques that ensure the most important features are correct with high-probability guarantees. These assess the set of $K$ top-ranked features, as well as the order of its elements. Given a set of local or global importance scores, we demonstrate how to retrospectively verify the stability of the highest ranks. We then introduce two efficient sampling algorithms that identify the $K$ most important features, perhaps in order, with probability exceeding $1-\alpha$. The theoretical justification for these procedures is validated empirically on SHAP and LIME.

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