Dear all,

For my research, I'm working with multi-output decision trees. In the current 
sklearn implementation, a tree can predict either several numerical or several 
categorical targets simultaneously, but not a mixture of those. However, 
predicting various targets jointly is often beneficial both in terms of speed 
and accuracy. Because of that, I'm willing to add this functionality.

It seems that the only thing to be done is to implement a new node splitting 
criteria that handles a mixture of nominal and numerical attributes, and then 
define a new class of models (such as DecisionTreeRegressor or

DecisionTreeClassifier, but for mixed output). However, since I'm not an 
experienced sklearn contributor, I am looking for any hints on how to implement 
this in effective way, re-using as much functionality already available as 

Your advice is very welcome.

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