It does not provide access for tracing the step by step feature weights and predictive ability- The user provides the n_feature.
Malik --------------------------------------------------------------------------------------- *Prof. Malik Yousef (Associate Professor) * *The Head of the** Galilee Digital Health Research Center (GDH)* *Zefat Academic College , Department of Information System * Home Page: https://malikyousef.com/ Google Scholar Profile : https://scholar.google.com/citations?user=9UCZ_q4AAAAJ&hl=en&oi=ao ---------------------------------------------------------------------------------------------------- On Mon, Nov 25, 2019 at 1:36 PM Brown J.B. via scikit-learn < scikit-learn@python.org> wrote: > > 2019年11月23日(土) 2:12 Andreas Mueller <t3k...@gmail.com>: > >> I think you can also use RFECV directly without doing any wrapping. >> >> Your request to do performance checking of the steps of SVM-RFE is a >> pretty common task. >> >> > Yes, RFECV works well (and I should know as an appreciative long-time user > ;-) ), but does it actually provide a mechanism (accessors) for tracing > the step by step feature weights and predictive ability as the features are > continually reduced? > (Or perhaps it's because I'm looking at 0.20.1 and 0.21.2 > documentation...?) > > J.B. > _______________________________________________ > 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
