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

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 level 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, t...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, Torsten Hothorn
• Keywords: BMCBI 2007, Random Forest, Variable Importance, Variable Selection