On Mon, Mar 25, 2013 at 9:19 PM, Andreas Mueller <amuel...@ais.uni-bonn.de> wrote:
> I'm confused. Since when is that so? The other losses definitely support > OvR multi-class. I would be quite surprised if 'log' does not. predict_proba currently raises an exception in the multiclass case: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/stochastic_gradient.py#L676 We can modifiy predict_proba so that it computes the probabilities of each one-vs-rest task and then normalize them, like we already do here: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/multiclass.py#L111 I'm not sure if there exists any strong theoretical motivation for doing this normalization but at least this is how liblinear is doing it and this is also the method recommended by this paper: http://www.research.ibm.com/people/z/zadrozny/kdd2002-Transf.pdf Mathieu ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_d2d_mar _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
