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 possible. Your advice is very welcome. Best, Evgeniya
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