  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.
