Just a comment: it would be a useful tool.
Отправлено с iPhone
> 12 февр. 2018 г., в 14:40, Evgeniya Korneva <evgeniya.korn...@kuleuven.be>
> 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.
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