Do Random Forest overfit? I have been reading around about Random Forests but I cannot really find a definitive answer about the problem of overfitting. According to the original paper of Breiman, they should not overfit when increasing the number of trees in the forest, but it seems that there is not consensus about this. This is creating me quite some confusion about the issue.

Maybe someone more expert than me can give me a more concrete answer or point me in the right direction to better understand the problem.
Every ML algorithm with high complexity can overfit. However, the OP is asking whether an RF will not overfit when increasing the number of trees in the forest.

In general, ensemble methods reduces the prediction variance to almost nothing, improving the accuracy of the ensemble. If we define the variance of the expected generalization error of an individual randomized model as:



From here, the variance of the expected generalization error of an ensemble corresponds to:



where p(x) is the Pearson’s correlation coefficient between the predictions of two randomized models trained on the same data from two independent seeds. If we increase the number of DT's in the RF, larger M, the variance of the ensemble decreases when ρ(x)&lt;1. Therefore, the variance of an ensemble is strictly smaller than the variance of an individual model. 

In a nutshell, increasing the number of individual randomized models in an ensemble will never increase the generalization error.