Bias in random forest variable importance measures: Illustrations, sources and a solution

TLDR

Variable importance measures for random forests are increasingly used for variable selection in bioinformatics and related fields, such as choosing genetic markers for disease prediction. The study proposes an alternative random forest implementation that removes bias in variable selection by addressing tree‑level bias and bootstrap sampling effects. The authors modify tree construction and employ subsampling without replacement, then demonstrate the unbiased algorithm in R using RNA‑editing data. The unbiased method produces reliable variable importance scores even when predictors vary in scale or number of categories, preventing misleading selection of suboptimal variables and enabling straightforward application by bioinformatics scientists.

Abstract

Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories.Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand.We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analyzing data from a study on RNA editing. Therefore the suggested method can be applied straightforwardly by scientists in bioinformatics research.