Just a comment: it would be a useful tool. -Dmitry
Отправлено с iPhone > 12 февр. 2018 г., в 14:40, Evgeniya Korneva <evgeniya.korn...@kuleuven.be> > написал(а): > > > 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 > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn
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