Statistics, Department of
The R Journal
Date of this Version
12-2009
Document Type
Article
Citation
The R Journal (December 2009) 1(2)
Abstract
Random forests are one of the most popular statistical learning algorithms, and a variety of methods for fitting random forests and related recursive partitioning approaches is available in R. This paper points out two important features of the random forest implementation cforest available in the party package: The resulting forests are unbiased and thus prefer able to the randomForest implementation avail able in randomForest if predictor variables are of different types. Moreover, a conditional per mutation importance measure has recently been added to the party package, which can help evaluate the importance of correlated predictor variables. The rationale of this new measure is illustrated and hands-on advice is given for the usage of recursive partitioning tools in R.
Included in
Numerical Analysis and Scientific Computing Commons, Programming Languages and Compilers Commons
Comments
Copyright 2009, The R Foundation. Open access material. License: CC BY 3.0 Unported