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Accurate estimation of feature importance faithfulness for tree models
April 5, 2024, 4:42 a.m. | Mateusz Gajewski, Adam Karczmarz, Mateusz Rapicki, Piotr Sankowski
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we consider a perturbation-based metric of predictive faithfulness of feature rankings (or attributions) that we call PGI squared. When applied to decision tree-based regression models, the metric can be computed accurately and efficiently for arbitrary independent feature perturbation distributions. In particular, the computation does not involve Monte Carlo sampling that has been typically used for computing similar metrics and which is inherently prone to inaccuracies. Moreover, we propose a method of ranking …
abstract arxiv call computation cs.lg decision feature importance independent paper predictive rankings regression tree type
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