[scikit-learn] Multi-Output Decision Trees for mixed classification-regerssion problems

[Дмитрий Игнатов]

Just a comment: it would be a useful tool.

-Dmitry

Отправлено с iPhone

> 12 февр. 2018 г., в 14:40, Evgeniya Korneva <evgeniya.korneva at 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
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