Assessing high-order effects in feature importance via predictability decomposition

TL;DR

This paper introduces an adaptive method to decompose feature importance into high-order effects, such as redundancy and synergy, enhancing interpretability in regression models.

Contribution

It proposes a novel adaptive LOCO-based approach to quantify and disentangle high-order interactions among features in explainable AI.

Findings

Effective decomposition of feature importance into high-order effects.

Application to particle discrimination demonstrates practical utility.

Highlights features contributing to redundancy and synergy.

Abstract

Leveraging the large body of work devoted in recent years to describe redundancy and synergy in multivariate interactions among random variables, we propose a novel approach to quantify cooperative effects in feature importance, one of the most used techniques for explainable artificial intelligence. In particular, we propose an adaptive version of a well-known metric of feature importance, named Leave One Covariate Out (LOCO), to disentangle high-order effects involving a given input feature in regression problems. LOCO is the reduction of the prediction error when the feature under consideration is added to the set of all the features used for regression. Instead of calculating the LOCO using all the features at hand, as in its standard version, our method searches for the multiplet of features that maximize LOCO and for the one that minimize it. This provides a decomposition of the LOCO as the sum of a two-body component and higher-order components (redundant and synergistic), also highlighting the features that contribute to building these high-order effects alongside the driving feature. We report the application to proton/pion discrimination from simulated detector measures by GEANT.