Tuning Parameters for Boosting/Bagging/Random Forest • /r/MachineLearning

Random forests usually performs quite well with the default settings. That is bootstrap resampling scheme, unpruned trees, as many trees as possible to get results in a reasonable amount of time and sqrt(#features) tried per split (mtry parameter). Then you can try to optimize the choices by checking the results on out of bag data (those each tree didnt train on because of the resampling scheme). If you have very unbalanced classes you should decide a measure of interest (such as true positive ratio) and try to tune the related parameter. Out of bag data can be trusted almost as a proper cross validation if you use enough trees and bootstrap resampling.