Hi Siddhant,

currently, we only provide two ensemble techniques: Random/Extra
Forest and Gradient Boosted Regression Trees - both use decision trees
as weak learners. Gradient Boosting itself can be used with different
base learners but AFAIK it hardly is*.

Unfortunately, scikit-learn lacks classical ensemble technique such as
Bagging or AdaBoost (there is a PR for AdaBoost though [1]); what kind
of techniques are you looking for (stacking, bagging, adaboost, ..)?

best,
 Peter

[1] https://github.com/scikit-learn/scikit-learn/pull/522

* mboost, http://cran.r-project.org/web/packages/mboost/index.html,
does component-wise (penalised) least squares too.

2012/10/15 Siddhant Goel <[email protected]>:
> Hi people,
>
> Does scikit-learn support plugging in user defined classifiers in its 
> ensemble learning framework? I went through the documentation but could only 
> find decision trees being used a weak learners. Is it possible to define our 
> own classifiers and then use an ensemble learning strategy using scikit? 
> Thanks!
>
> Siddhant
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-- 
Peter Prettenhofer

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