[scikit-learn] SVM-RFE

Brown J.B. jbbrown at kuhp.kyoto-u.ac.jp
Thu Dec 5 00:14:35 EST 2019

I certainly am guilty of only commenting in the mailing list and not
engaging more via GitHub! :)
(Much like many of you PIs on this list, the typical
cycle eats the day away.)

While I've failed previously to get involved after showing interest, let's
see if I can't actually succeed for once.

2019年12月5日(木) 1:14 Andreas Mueller <t3kcit at gmail.com>:

> PR welcome ;)
> On 12/3/19 11:02 PM, Brown J.B. via scikit-learn wrote:
> 2019年12月3日(火) 5:36 Andreas Mueller <t3kcit at gmail.com>:
>> 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.
> I would guess that there is some population of the user base that would
> like to track the per-iteration feature weights.
> It would appear to me that a straightforward (un-optimized) implementation
> would be place a NaN value for a feature once it is eliminated, so that a
> numpy.ndarray can be returned and immediately dumped to
> matplotlib.pcolormesh or other visualization routines in various libraries.
> Just an idea.
> J.B.
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