Prediction via Shapley Value Regression
Journal:
arXiv
Published Date:
May 7, 2025
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.
