I addition to checking _n_iter and fixing the random seed as I suggested maybe also try normalizing the features (eg z scores via the standard scale we) to see if that stabilizes the training
Sent from my iPhone > On Jul 24, 2018, at 1:07 PM, Benoît Presles <benoit.pres...@u-bourgogne.fr> > wrote: > > I did the same tests as before adding fit_intercept=False and: > > 1. I have got the same problem as before, i.e. when I execute the RFE > multiple times I don't get the same ranking each time. > > 2. When I change the solver to 'sag' > (classifier_RFE=LogisticRegression(C=1e9, verbose=1, max_iter=10000, > fit_intercept=False, solver='sag')), it seems that I get the same ranking at > each run. This is not the case with the 'saga' solver. > The ranking is not the same between the solvers. > > 3. With C=1, it seems that I have the same results at each run for all > solvers (liblinear, sag and saga), however the ranking is not the same > between the solvers. > > > How can I get reproducible and consistent results? > > > Thanks for your help, > Best regards, > Ben > > > >> Le 24/07/2018 à 18:16, Stuart Reynolds a écrit : >> 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 > > _______________________________________________ > 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
