Hi, recently a MultiOutput* "adaptor" was added to scikit-learn (-dev version only so far I think). They take algorithms like GradientBoosting* and fit one instance per target wrapped in a nice interface. You won't be able to take advantage of correlations between the outputs this way but it might be a starting point. Take a look at:
http://scikit-learn.org/dev/modules/classes.html#module-sklearn.multioutput http://scikit-learn.org/dev/auto_examples/ensemble/plot_random_forest_regression_multioutput.html T On Mon, May 30, 2016 at 6:16 PM Peter Prettenhofer < [email protected]> wrote: > Hi Roberto, > > correct - GradientBoostingRegressor | GradientBoostingClassifier does not > support multiple outputs. > > best, > Peter > > 2016-05-30 16:05 GMT+02:00 Roberto Pagliari <[email protected]>: > >> I noticed that the fit method of GBR does not return a [n_samples, >> n_output] array. Does that mean multiple output variables are not supported? >> >> I'm asking because most other regressors do. >> >> Thank you, >> >> >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > > -- > Peter Prettenhofer > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn >
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