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arXiv Machine Learning By Garvesh Raskutti, Kris Sankaran, Jiaxin Ye

Null importance: Disentangling relevance for interpretable machine learning

Read the original on arXiv Machine Learning →

The paper introduces a unified framework called null importance to clarify different notions of feature relevance in interpretable machine learning. It defines null importance at the population level for various relevance concepts—marginal, conditional, predictive risk, functional invariance, and causal effects—and demonstrates how each answers distinct scientific questions. Through theoretical analysis, simulations, and case studies on fairness and genomic modeling, the authors show when these null notions coincide or diverge and how different importance methods target them.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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