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arXiv · 2412.13864

Constructing sensible baselines for Integrated Gradients

Jai Bardhan·Cyrin Neeraj·Mihir Rawat·Subhadip Mitra
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Abstract

Machine learning methods have seen a meteoric rise in their applications in the scientific community. However, little effort has been put into understanding these "black box" models. We show how one can apply integrated gradients (IGs) to understand these models by designing different baselines, by taking an example case study in particle physics. We find that the zero-vector baseline does not provide good feature attributions and that an averaged baseline sampled from the background events provides consistently more reasonable attributions.

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Jai Bardhan, Cyrin Neeraj, Mihir Rawat, Subhadip Mitra. 2024-12-18. Constructing sensible baselines for Integrated Gradients. https://arxiv.org/abs/2412.13864

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