Abstract
BackgroundRandom 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.ResultsWe 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.ConclusionThe resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach.
Highlights
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
The variable importance measures yielded by random forests have been suggested for the selection of relevant predictor variables in the analysis of microarray data, DNA sequencing and other applications [2,3,4,5]
In this case a key advantage of random forest variable importance measures, as compared to univariate screening methods, is that they cover the impact of each predictor variable individu
Summary
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. Within the past few years, random forests [1] have become a popular and widely-used tool for non-parametric regression in many scientific areas They show high predictive accuracy and are applicable even in highdimensional problems with highly correlated variables, a situation which often occurs in bioinformatics. In this case a key advantage of random forest variable importance measures, as compared to univariate screening methods, is that they cover the impact of each predictor variable individu-
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