title: Conditional variable importance for random forests.
abstract: BACKGROUND
  Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables.
  
  
  RESULTS
  We identify two mechanisms responsible for this finding: (i) A preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme that is employed in the computation of the variable importance measure. Based on these considerations we develop a new, conditional permutation scheme for the computation of the variable importance measure.
  
  
  CONCLUSIONS
  The resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach.
authors.affiliation: Department of Statistics, Ludwig-Maximilians-Universität Munchen, Ludwigstrasse 33, D-80539 München, Germany. carolin.strobl@stat.uni-muenchen.de
authors.forename: Carolin
authors.initials: C
authors.lastname: Strobl
authors.forename: Anne-Laure
authors.initials: AL
authors.lastname: Boulesteix
authors.forename: Thomas
authors.initials: T
authors.lastname: Kneib
authors.forename: Thomas
authors.initials: T
authors.lastname: Augustin
authors.forename: Achim
authors.initials: A
authors.lastname: Zeileis
mesh_terms: D000595:Amino Acid Sequence; D001665:Binding Sites; D001699:Biometry / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D003663:Decision Trees; D005163:Factor Analysis, Statistical; D008285:Major Histocompatibility Complex / Q000235:genetics; D012044:Regression Analysis; D012107:Research Design; D018709:Statistics, Nonparametric