Shapley Feature Selection

Alex Gramegna and Paolo Giudici
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Alex Gramegna: Department of Economics and Management, Neosurance and University of Pavia, 27100 Pavia, PV, Italy

FinTech, 2022, vol. 1, issue 1, 1-9

Abstract: Feature selection is a popular topic. The main approaches to deal with it fall into the three main categories of filters, wrappers and embedded methods. Advancement in algorithms, though proving fruitful, may be not enough. We propose to integrate an explainable AI approach, based on Shapley values, to provide more accurate information for feature selection. We test our proposal in a real setting, which concerns the prediction of the probability of default of Small and Medium Enterprises. Our results show that the integrated approach may indeed prove fruitful to some feature selection methods, in particular more parsimonious ones like LASSO. In general the combination of approaches seems to provide useful information which feature selection algorithm can improve their performance with.

Keywords: machine learning; variable selection; credit scoring (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2022
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