Related papers: Provably Stable Feature Rankings with SHAP and LIME

Provably Stable Feature Rankings with SHAP and LIME

URL: http://arxiv.org/abs/2401.15800v2
Date: Mon, 3 Jun 2024 00:49:43 GMT
Title: Provably Stable Feature Rankings with SHAP and LIME
Authors: Jeremy Goldwasser, Giles Hooker,
Abstract summary: We devise attribution methods that ensure the most important features are ranked correctly with high probability. We introduce efficient sampling algorithms for SHAP and LIME that guarantee the $K$ highest-ranked features have the proper ordering.
Score: 3.8642937395065124
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Feature attributions are ubiquitous tools for understanding the predictions of machine learning models. However, the calculation of popular methods for scoring input variables such as SHAP and LIME suffers from high instability due to random sampling. Leveraging ideas from multiple hypothesis testing, we devise attribution methods that ensure the most important features are ranked correctly with high probability. Given SHAP estimates from KernelSHAP or Shapley Sampling, we demonstrate how to retrospectively verify the number of stable rankings. Further, we introduce efficient sampling algorithms for SHAP and LIME that guarantee the $K$ highest-ranked features have the proper ordering. Finally, we show how to adapt these local feature attribution methods for the global importance setting.

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