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
