liblinear regularizes the intercept (which is a questionable thing to do and a poor choice of default in sklearn). The other solvers do not.
On Tue, Jul 24, 2018 at 4:07 AM, Benoît Presles <benoit.pres...@u-bourgogne.fr> wrote: > Dear scikit-learn users, > > I am using the recursive feature elimination (RFE) tool from sklearn to rank > my features: > > from sklearn.linear_model import LogisticRegression > classifier_RFE = LogisticRegression(C=1e9, verbose=1, max_iter=10000) > from sklearn.feature_selection import RFE > rfe = RFE(estimator=classifier_RFE, n_features_to_select=1, step=1) > rfe.fit(X, y) > ranking = rfe.ranking_ > print(ranking) > > 1. The first problem I have is when I execute the above code multiple times, > I don't get the same results. > > 2. When I change the solver to 'sag' or 'saga' (classifier_RFE = > LogisticRegression(C=1e9, verbose=1, max_iter=10000), solver='sag'), it > seems that I get the same results at each run but the ranking is not the > same between these two solvers. > > 3. With C=1, it seems I have the same results at each run for the > solver='liblinear', but not for the solvers 'sag' and 'saga'. I still don't > get the same results between the different solvers. > > > Thanks for your help, > Best regards, > Ben > > _______________________________________________ > 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
