LLpowershap: Logistic Loss-based Automated Shapley Values Feature Selection Method

TL;DR

LLpowershap is a new feature selection method using loss-based Shapley values, which effectively identifies informative features with less noise and achieves competitive predictive performance on real-world datasets.

Contribution

Introduces LLpowershap, a novel loss-based Shapley value method for feature selection that outperforms existing methods in identifying relevant features with minimal noise.

Findings

LLpowershap detects more informative features than competing methods.

It produces fewer noise features in selected sets.

Achieves higher or comparable predictive accuracy on real datasets.

Abstract

Shapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, a number of feature selection methods utilising Shapley values have been introduced. Here, we present a novel feature selection method, LLpowershap, which makes use of loss-based Shapley values to identify informative features with minimal noise among the selected sets of features. Our simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods. Benchmarking results on four real-world datasets demonstrate higher or at par predictive performance of LLpowershap compared to other Shapley based wrapper methods, or filter methods.