Suppose we have a dataset where the majority of the features are categorical. Do tree-based methods (such as decision trees, random forests and boosting) generally outperform other classification models on such a dataset?
I am aware of this question that discusses some of the potential computational advantages of using tree-based methods with categorical variables. My question is about accuracy/precision and not concerned with computational time.
