Thanks, Tom, that makes sense. Submitted a PR to fix that. Best, Sebastian
> On Dec 19, 2016, at 10:14 AM, Tom DLT <tom.duprelat...@orange.fr> wrote: > > Hi, > > In LogisticRegression, n_jobs is only used for one-vs-rest parallelization. > In LogisticRegressionCV, n_jobs is used for both one-vs-rest and > cross-validation parallelizations. > > So in LogisticRegression with multi_class='multinomial', n_jobs should have > no impact. > > The docstring should probably be updated as you mentioned. PR welcome :) > > Best, > Tom > > 2016-12-19 6:13 GMT+01:00 Sebastian Raschka <se.rasc...@gmail.com>: > Hi, > > I just got confused what exactly n_jobs does for LogisticRegression. Always > thought that it was used for one-vs-rest learning, fitting the models for > binary classification in parallel. However, it also seem to do sth in the > multinomial case (at least according to the verbose option). in the docstring > it says > > > n_jobs : int, optional > > Number of CPU cores used during the cross-validation loop. If given > > a value of -1, all cores are used. > > and I saw a logistic_regression_path being defined in the code. I am > wondering, is this just a workaround for the LogisticRegressionCV, and should > the n_jobs docstring in LogisticRegression > be described as "Number of CPU cores used for model fitting” instead of > “during cross-validation,” or am I getting this wrong? > > Best, > Sebastian > _______________________________________________ > 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
