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

    Strobl, Carolin; Boulesteix, Anne-Laure; Zeileis, Achim; Hothorn, Torsten
    Background: 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...
    (application/pdf) - 18-oct-2016

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