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PR welcome ;)<br>
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<div class="moz-cite-prefix">On 12/3/19 11:02 PM, Brown J.B. via
scikit-learn wrote:<br>
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<div dir="ltr" class="gmail_attr">2019年12月3日(火) 5:36 Andreas
Mueller <<a href="mailto:t3kcit@gmail.com"
moz-do-not-send="true">t3kcit@gmail.com</a>>:<br>
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<div> It does provide the ranking of features in the
ranking_ attribute and it provides the cross-validation
accuracies for all subsets in grid_scores_.<br>
It doesn't provide the feature weights for all subsets,
but that's something that would be easy to add if it's
desired.<br>
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<div>I would guess that there is some population of the user
base that would like to track the per-iteration feature
weights.</div>
<div>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.<br>
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<div>Just an idea.</div>
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<div>J.B.</div>
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