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 <t3k...@gmail.com
<mailto:t3k...@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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