Boosting is one example of a "slow learning" approach: even when we can get a very good fit to the current residuals with a single model, we don't just run with it, but take a "part" of that knowledge (represented by the learning rate parameter lambda) and wait to see that maybe some other model (tree in our case) can get an equally good fit - in layman terms, if the fact "can have two explanations" then it might be better to remember both those explanations rather than just memorize the first explanation we encounter and consider it done. It can be claimed that *in general* slow learning approaches tend to perform better than "greedy" ones, regardless of the particular model used (i.e. whether it is a decision tree or something else).

True

False