i have been wondering for some time now how random forests (or AdaBoost, doesn't matter) are built when using cross-validation. Let's see we're using 5-fold cross validation to train random forests on 5 different training sets and therefore test on 5 different test sets. How does the 'final' random forest look like when we are basically building 5 random forests (one for each fold of the cross validation). How are these forests combined into a final model?
I have never understood this step and I really hope someone can help me with this!
thanks in advance, Steven