Variable selection using random forests
Description
Variable selection from random forests using both
backwards variable elimination (for the selection of small sets
of non-redundant variables) and selection based on the
importance spectrum (somewhat similar to scree plots; for the
selection of large, potentially highly-correlated variables).
Main applications in high-dimensional data (e.g., microarray
data, and other genomics and proteomics applications). You can
use rpvm instead of Rmpi if you want but I've only tested with
Rmpi.