Prediction via Shapley Value Regression

• URL: http://arxiv.org/abs/2505.04775v2
• Date: Mon, 14 Jul 2025 22:55:55 GMT
• Title: Prediction via Shapley Value Regression
• Authors: Amr Alkhatib, Roman Bresson, Henrik Boström, Michalis Vazirgiannis,
• Abstract summary: A novel method, called ViaSHAP, is proposed that learns a function to compute Shapley values, from which the predictions can be derived directly by summation.<n>Results from a large-scale empirical investigation are presented.
• Score: 18.708235771482205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shapley values have several desirable, theoretically well-supported, properties for explaining black-box model predictions. Traditionally, Shapley values are computed post-hoc, leading to additional computational cost at inference time. To overcome this, a novel method, called ViaSHAP, is proposed, that learns a function to compute Shapley values, from which the predictions can be derived directly by summation. Two approaches to implement the proposed method are explored; one based on the universal approximation theorem and the other on the Kolmogorov-Arnold representation theorem. Results from a large-scale empirical investigation are presented, showing that ViaSHAP using Kolmogorov-Arnold Networks performs on par with state-of-the-art algorithms for tabular data. It is also shown that the explanations of ViaSHAP are significantly more accurate than the popular approximator FastSHAP on both tabular data and images.

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