I have a conceptual question. My understanding is, that Random Forest can be applied even when features are (highly) correlated. This is because with bagging, the influence of few highly correlated features is moderated, since each feature only occurs in some of the trees which are finally used to build the overall model.
My question: With boosting, usually even smaller trees (basically "stunps") are used. Is it a problem to have many (highly) correlated features in a bagging approach?