How to control number of branches for each split in sklearn DT classifier? I am trying to code a two class classification DT problem that I used SAS EM before. But trying to do it in Sklearn. The target variable is a two class categorical variable. But there are a few continuous independent variables. In SAS I could specify the "Maximum Number of Branches" for each split. So when it is set to 4, some leaf will split into 2 and some in 4 (especially for continuous variables). I could not find an equivalent parameter in sklearn. Looked at "max_leaf-nodes". But that controls the total number of "leaf" nodes of the entire tree. I am sure some of you probably has faced the same situation and already found a solution. Please help/share. I will really appreciate it.
Scikit-learn uses an optimized version of the CART algorithm, which follows binary splits. What you are looking for is a multi-way split on nodes for information gain. 

You should look for a C4.5 or C5.0 implementation of Decision tress in python.