Mmm
Maybe just including the simple pipeline you provide in the feature
selection doc [1] would suffice to point to the recommended way to do that ?
Like a sub-sub-section dubbed "Including feature selection in a prediction
pipeline" ?
What do you think ?
Would it be too detailed ? should we let users figure this out themselves ?
[1] http://scikit-learn.org/dev/modules/feature_selection.html
2013/7/16 Joel Nothman <[email protected]>
> For your example, Eustache, the following would work (with a dense or
> sparse X):
>
> """
> clf = GradientBoostingClassifier()
> clf.fit(X, y)
> clf.fit(clf.transform(threshold=1e-3), y)
> """
>
> Alternatively, use a Pipeline:
> """
> clf = Pipeline([
> ('sel', GradientBoostingClassifier()),
> ('clf', GradientBoostingClassifier())
> ])
> clf.fit(X, y)
> """
> This will apply the default threshold (1e-5); currently the threshold
> can't be set for use in a pipeline, pending an issue that I can't currently
> locate, which would move the threshold to the object as with randomized
> l1's selection_threshold parameters.
>
> The Pipeline examples include feature selectors, if only univariate. Is
> there somewhere in the documentation you think these could be clearer? If
> so, submit a PR.
>
> - Joel
>
>
> On Wed, Jul 17, 2013 at 3:49 AM, Olivier Grisel
> <[email protected]>wrote:
>
>> Feature selectors should implement the `Transformer` API so that they
>> can be used in a Pipeline and make it possible to cross validate them.
>>
>> The univariate feature selectors already implement the transformer API:
>>
>>
>> http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection
>>
>> --
>> Olivier
>>
>>
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>
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