Hi,
1) It’s not possible to perform subsampling in RandomForestRegressor and
RandomForestClassifier. However, you can use the BaggingClassifier and the
BaggingRegressor to achieve that.
2) The number of features drawn at each during the tree growth is control by
the max_features parameters. If you want to perform random subspace, you can
have a look to BaggingClassifier and BaggingRegressor.
3) It’s possible to achieve 1 and 2 using both the bagging and random forest
estimators.
Best regards,
Arnaud Joly
On 16 Dec 2014, at 09:06, Miquel Camprodon
<miquel.campro...@kernel-analytics.com> wrote:
> Hi all,
>
> I am a newbie in the use of scikit-learn. Congratulations for the project!
>
> (1) I am using the ensemble methods RandomForestRegressor and
> RandomForestClassifier. I would like to adjust the number of subsamples each
> estimator will use. How can I achieve this? My background is in R, and in
> that language there is the sampsize parameter which allows to control it.
> If it is not possible, how are subsamples drawn for each estimator in
> scikit-learn?
>
> (2) How can I parametrize the number of features each estimator will use?
>
> (3) If the answer is that I should write my own ensemble, can you give me
> some hints about how to do it?
>
> Thanks a lot,
>
> Miquel
>
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