title: Recursive feature elimination in 'caret' for 'randomForest': set different ntree parameter for the first forest
body: <p>I am currently trying to optimize the random forest classifier for a very high-dimensional dataset (p > 200k) using recursive feature elimination (RFE). <a href="http://cran.r-project.org/web/packages/caret/index.html" rel="nofollow"><strong>caret</strong></a> package has a nice implementation for doing this (<strong><em>rfe()</em></strong>-function). However, I am also thinking about optimizing RAM and CPU usage.. That's why I wonder if there is an opportunity to set different (larger) number of trees to train the first forest (without feature elimination) and to use its importances to build the remaining ones (with RFE) using for example 500 trees with 10- or 5-fold cross-validation. I know that this option is available in <a href="http://cran.r-project.org/web/packages/varSelRF/index.html" rel="nofollow">varSelRF</a>.. But how about <a href="http://cran.r-project.org/web/packages/caret/index.html" rel="nofollow"><strong>caret</strong></a>? I didn't manage to find anything regarding this in the manual.</p>