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A Guide to Feature Importance Methods for Scientific Inference
April 22, 2024, 4:42 a.m. | Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar K\"onig
cs.LG updates on arXiv.org arxiv.org
Abstract: While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding the DGP requires insights into feature-target associations, which many ML models cannot directly provide, due to their opaque internal mechanisms. Feature importance (FI) methods provide useful insights into the DGP under certain conditions. Since the results of different FI methods have different interpretations, selecting the correct FI method for a …
abstract arxiv cs.lg data feature guide importance inference insights machine machine learning math.st ml models power predictive process scientific stat.me stat.ml stat.th type understanding
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