u/Latter_Cricket_3292

Hi,

Am studying nowadays decision trees from Hands on ML book. It mentioned at the end of the chapter that decision trees are highly sensitive to small variation on the data so it's better using Random Forest. It just doesn't click with me. Isn't using large dataset with proper regularization solve the variance problem? I know that with slight changes in the data the splits in the tree may differ and the whole following branch will have different splits as well. But whats the problem with that? if we tested the modelling process and the set of hyperparameters generalize well on unseen data so why can't we rely on it. I just feel books and communities just overskip trees to RF directly. Am I missing sth?