[scikit-learn] SVM-RFE

Brown J.B. jbbrown at kuhp.kyoto-u.ac.jp
Tue Dec 3 23:02:28 EST 2019

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.

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