What is more, Leo Breimain wrote on his website: "Random forests does not overfit. You can run as many trees as you want" https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home...
Deep trees will fortunately overfit your dataset.
Any binary tree of depth log2(P) can completely separate your P points.
False claims as they maybe, these are claims I've seen in at least two of the most commonly studied statistical learning text books, so given that it makes sense and that it's in the text books, it seems reasonably not false to me. Someone else posted that if too many features or data points are very similar then it will overfit, and that totally makes sense. Whatever you say doesnt. Clarification would be useful.
I have an explanation here why reducing variance is not the same as reducing overfitting: https://news.ycombinator.com/item?id=20089890