Solved – Random Forest can’t overfit

Question

I've read some literature that random forests can't overfit. While this sounds great, it seems too good to be true. Is it possible for rf's to overfit?

Answer 1

Random forest can overfit. I am sure of this. What is usually meant is that the model would not overfit if you use more trees.

Try for example to estimate the model $y = log(x) + \epsilon$ with a random forest. You will get an almost zero training error but a bad prediction error