LLpowershap: Logistic Loss-based Automated Shapley Values Feature Selection Method
This work addresses feature selection for robust modeling in machine learning, offering an incremental improvement over existing Shapley-based methods.
The authors tackled the problem of feature selection by introducing LLpowershap, a novel method that uses loss-based Shapley values to identify informative features with minimal noise, resulting in higher numbers of informative features and fewer noise features in simulations, and competitive or superior predictive performance on real-world datasets.
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.