[scikit-learn] SVM-RFE

[Andreas Mueller]

It does provide the ranking of features in the ranking_ attribute and it 
provides the cross-validation accuracies for all subsets in grid_scores_.
It doesn't provide the feature weights for all subsets, but that's 
something that would be easy to add if it's desired.



On 11/25/19 10:50 AM, Malik Yousef wrote:
> It does not provide access for tracing the step by step feature 
> weights and predictive ability- The user provides the n_feature.
>
> Malik
>
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> *Prof. Malik Yousef (Associate Professor) *
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>
> On Mon, Nov 25, 2019 at 1:36 PM Brown J.B. via scikit-learn 
> <scikit-learn at python.org <mailto:scikit-learn at python.org>> wrote:
>
>
>     2019年11月23日(土) 2:12 Andreas Mueller <t3kcit at gmail.com
>     <mailto:t3kcit at gmail.com>>:
>
>         I think you can also use RFECV directly without doing any
>         wrapping.
>
>>         Your request to do performance checking of the steps of
>>         SVM-RFE is a pretty common task.
>
>
>     Yes, RFECV works well (and I should know as an appreciative
>     long-time user ;-)  ), but does it actually provide a mechanism
>     (accessors) for tracing the step by step feature weights and
>     predictive ability as the features are continually reduced?
>     (Or perhaps it's because I'm looking at 0.20.1 and 0.21.2
>     documentation...?)
>
>     J.B.
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