Identifying Informative Predictor Variables With Random Forests

Abstract

Random forests are a nonparametric machine learning method, which is currently gaining popularity in the behavioral sciences. Despite random forests’ potential advantages over more conventional statistical methods, a remaining question is how reliably informative predictor variables can be identified by means of random forests. The present study aims at giving a comprehensible introduction to the topic of variable selection with random forests and providing an overview of the currently proposed selection methods. Using simulation studies, the variable selection methods are examined regarding their statistical properties, and comparisons between their performances and the performance of a conventional linear model are drawn. Advantages and disadvantages of the examined methods are discussed, and practical recommendations for the use of random forests for variable selection are given.