It makes complete sense that Random Forests decrease variance by only considering a random subset of features at each split for each individual decision tree, thus leading to uncorrelated trees.

However, what doesn’t make sense to me is — why wouldn’t this increase the bias to the point where the reduction in variance didn’t really matter? If the trees are all biased enough, then it doesn’t matter if variance is removed via averaging — the ensemble will still be bad due to the bias.

Does anyone have intuition as to why the bias isn’t increased too much?