How is the result from a Decision Tree Regressor continuous?
For anyone searching for what I was searching for:
There are combination of Linear/Log/... Regression Models with Decision Trees, called "Model Tree". Unfortunately not included in any free python package, but there are some implementations on github, if you google it.It is also supposed to be available soon with scikit-learn.
The Model Tree is simular to the M5 algorithm.
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.