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_regres... T On Mon, May 30, 2016 at 6:16 PM Peter Prettenhofer < peter.prettenhofer@gmail.com> wrote:

Hi Roberto,

correct - GradientBoostingRegressor | GradientBoostingClassifier does not support multiple outputs.

best, Peter

2016-05-30 16:05 GMT+02:00 Roberto Pagliari <roberto.pagliari@asos.com>:

...

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,

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