On 05/14/2013 04:52 PM, Gael Varoquaux wrote:
> On Tue, May 14, 2013 at 06:54:32AM +1000, Joel Nothman wrote:
>> Should the new SelectorMixin be part of the public API?
> I don't think so.
>
None of the Mixins is.
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On Tue, May 14, 2013 at 06:54:32AM +1000, Joel Nothman wrote:
> Should the new SelectorMixin be part of the public API?
I don't think so.
G
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Well, if the naming's alright, I think PR#1962 should be a no-brainer. I
also added a deprecation warning in case SelectorMixin was used externally.
On Tue, May 14, 2013 at 7:00 AM, Lars Buitinck wrote:
> 2013/5/13 Joel Nothman :
> > How about SelectorMixin and _LearntSelectorMixin respectively
2013/5/13 Joel Nothman :
> How about SelectorMixin and _LearntSelectorMixin respectively?
SelectKBest and friends also learn.
> On Tue, May 14, 2013 at 6:54 AM, Joel Nothman
> wrote:
>>
>> That seems a happy solution to me! Should the new SelectorMixin be part of
>> the public API?
I haven't se
How about SelectorMixin and _LearntSelectorMixin respectively?
On Tue, May 14, 2013 at 6:54 AM, Joel Nothman
wrote:
> That seems a happy solution to me! Should the new SelectorMixin be part of
> the public API?
>
> (I'm not sure about Clf on _ClfSelectorMixin when it's mixed into
> regressors to
That seems a happy solution to me! Should the new SelectorMixin be part of
the public API?
(I'm not sure about Clf on _ClfSelectorMixin when it's mixed into
regressors too. Or aren't we so particular? Really what we mean is
_FeatureSelectorFromLearntModelParametersMixin, but terser.)
On Mon, May
On Mon, May 13, 2013 at 03:07:12PM +0200, Lars Buitinck wrote:
> Since SelectorMixin is not exported by sklearn.feature_selection and
> is not documented anywhere, we can regard it as a private part of the
> API; if users have to read the source to find it, it's fair game. I
> suggest we rename tha
2013/5/13 Joel Nothman :
> There I name it FeatureSelectionMixin, but there is already a
> sklearn.feature_selection.selector_mixin.SelectorMixin for general
> estimators which also assign features importances (or they can be inferred
> from coefs_). This naming is potentially confusing, so I was w
Much of the functionality now in
sklearn.feature_selection.univariate_selection._BaseFilter [1] applies more
generally to all feature selection: extracting a column subset of some X
given some mask (transform), reversing that operation (inverse_transform)
and reporting the mask itself (get_support)
