I have used sklearn.tree.DecisionTreeRegressor to predict a regression problem with two independables aka the features "X", "Y" and the predicted dependable variable "Z". When I plot the tree, the leafs do not seem to differ much from a Classification tree. The result is not a function at each leaf, but it is a single value at each leaf, just like in a classification.
Can someone explain, why this is called a regression and why it is different to a classification tree?
Because I seem to have misunderstood the sklearn class, is there a tree package for python, that does a "real" regression and has a function as an output at each leaf? With X,Y and Z, this would probably be some kind of surface at each leaf.