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 ActualWork-GrantWriting-ReportWriting-InvitedLectures-RealLifeParenting 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 <t3k...@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 <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. > > _______________________________________________ > scikit-learn mailing > listscikit-learn@python.orghttps://mail.python.org/mailman/listinfo/scikit-learn > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
