On Mon, 4 Jul 2016 at 15:33 Tom DLT <tom.duprelat...@orange.fr> wrote:
> note2: > > The LogisticRegression and Ridge(solver='sag') code do fit the intercept > without breaking sparsity. > > For other solvers in Ridge, in the case of a sparse X input, the solver > will automatically be changed to 'sag' and raise a warning. > > Tom > > 2016-07-04 7:24 GMT+02:00 Tom Dupré la Tour <tom.duprelatour...@gmail.com> > : > >> note2: >> >> The LogisticRegression and Ridge(solver='sag') code do fit the intercept >> without breaking sparsity. >> >> For other solvers in Ridge, in the case of a sparse X input, the solver >> will automatically be changed to 'sag' and raise a warning. >> > Thanks. I understand that these estimators can fit the intercept without breaking the sparsity. My point was, would it not be useful to raise a warning when the input is sparse and the user does _not_ want to fit the intercept? > Le 2 juil. 2016 15:48, "Alexandre Gramfort" < >> alexandre.gramf...@telecom-paristech.fr> a écrit : >> >>> note: >>> >>> the Lasso and ElasticNet code do fit the intercept without breaking >>> sparsity. >>> >>> Alex >>> _______________________________________________ >>> 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 >
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