Suppose I am using random forests where the classes are highly unbalanced. How do you detect over fitting and what can you do to avoid it? Breiman says in his paper that random forests do not overfit, but others say that they can? If overfitting does exist(i.e. correlated trees) what is the best course of action to counteract that, and why does Breiman say that Random Forests are impervious to overfitting?
Also, how do you deal with the fact that the class you want to predict is percent-wise so small(suppose you have 99% 1's and 1% 0's)? What are some key metrics to measure the overall model fit and how do you go about testing and training in such cases?