For example I use fit_intercept=False when training SVMRank-style models where inputs are pairwise differences (x_i - x_j), I[y_i > y_j]. In this setting it's actually incorrect to learn an intercept.
On Tue, Jul 5, 2016 at 3:46 AM, Gael Varoquaux <gael.varoqu...@normalesup.org> wrote: >> > Jaidev is suggesting that fit_intercept=False makes no sense if the >> > data is sparse. But I think that depends on your target variable. > >> It can make sense **not** to fit intercept e.g. if it has no impact on >> perf it is faster to optimize without one > > +1 > _______________________________________________ > 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
