

The following report by the authors of the randomForest package describes two different algorithm modifications for using random forests to learn classifiers for "unbalanced" learning problems in which one class is much less frequent than the other (in 2-class problems). These two variations are called "balanced RF" and "weighted RF."
http://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf


Would someone please answer these three questions.
(1) Is it possible to use the R randomForest package to learn random forests using either of these modified RF-learning algorithms? 
(2) If it is possible, how does one do it?
(3) Is there some detailed documentation for running these modified versions? I've read the R package manual but it's too sketchy. It seems to be primarily for users who are already familiar with the package and just need to look up some detail like the name of an argument.

	[[alternative HTML version deleted]]

