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arXiv · 1909.01869

Generalized Integrated Gradients: A practical method for explaining diverse ensembles

John Merrill·Geoff Ward·Sean Kamkar·Jay Budzik·Douglas Merrill
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Abstract

We introduce Generalized Integrated Gradients (GIG), a formal extension of the Integrated Gradients (IG) (Sundararajan et al., 2017) method for attributing credit to the input variables of a predictive model. GIG improves IG by explaining a broader variety of functions that arise from practical applications of ML in domains like financial services. GIG is constructed to overcome limitations of Shapley (1953) and Aumann-Shapley (1974), and has desirable properties when compared to other approaches. We prove GIG is the only correct method, under a small set of reasonable axioms, for providing explanations for mixed-type models or games. We describe the implementation, and present results of experiments on several datasets and systems of models.

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John Merrill, Geoff Ward, Sean Kamkar, Jay Budzik, Douglas Merrill. 2019-09-04. Generalized Integrated Gradients: A practical method for explaining diverse ensembles. https://arxiv.org/abs/1909.01869

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