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) *
*The Head of the** Galilee Digital Health Research Center (GDH)***
*Zefat Academic College , Department of Information System *
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On Mon, Nov 25, 2019 at 1:36 PM Brown J.B. via scikit-learn
<scikit-learn@python.org <mailto:scikit-learn@python.org>> wrote:
2019年11月23日(土) 2:12 Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@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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