Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach.

ABSTRACT: Random forests (RFs) are a widely used modelling tool capable of feature selection via a variable importance measure (VIM), however, a threshold is needed to control for false positives. In the absence of a good understanding of the characteristics of VIMs, many current approaches attempt to select features associated to the response by training multiple RFs to generate statistical power via a permutation null, by employing recursive feature elimination, or through a combination of both. However, for high-dimensional datasets these approaches become computationally infeasible. In this paper, we present RFlocalfdr, a statistical approach, built on the empirical Bayes argument of Efron, for thresholding mean decrease in impurity (MDI) importances. It identifies features significantly associa

SUBMITTER: Dunne R 

PROVIDER: S-EPMC10497997 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature