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  1. Conditional Variable Importance for Random Forests

    Strobl, Carolin; Boulesteix, Anne-Laure; Kneib, Thomas; Augustin, Thomas; Zeileis, Achim
    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...
    (application/pdf) - 18-oct-2016

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