I've read other questions regarding if a continuous feature should be converted to categorical or not. But I'm interested in case of tree based classifiers such as Decision Tree, Random Forest, Gradient Boosted etc.
My intuition is that since tree based classifiers try to find the optimal split or the best test at each node, providing a categorical feature would make the splits more accurate than a providing a continuous feature.
My question is, doing the aforementioned pre-processing of data will lead to high accuracy in case of tree based models or the opposite? or it depends on the data?