[scikit-learn] gradient boosting regression with multiple output variables?

[Tim Head]

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 <
peter.prettenhofer at 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 at 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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>>
>>
>
>
> --
> Peter Prettenhofer
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