Hi Herbert The worst value for AUC is 0.5 actually. Having values close to 0 means than you can get a value as close to 1 by just changing your predictions (predict class 1 when you think it's 0 and vice versa). Are you sure you didn't confuse classes somewhere along the lines? (You might have chosen the wrong column from predict_proba's result, for example)
On Tue, Aug 4, 2015 at 4:51 PM, Herbert Schulz <hrbrt....@gmail.com> wrote: > Hey, > > I'm computing the AUC for some data... > > > The classification target is 1 or 0. And i have a lot of 0's ( 5600) and > just 700 1's as a target. > > My AUC is about 0.097... > > where y_test are a vector containing 1's and 0's and auc is containg the > predict_proba values > > roc= metrics.roc_auc_score(y_test, auc). > > > Actually this value seems way to bad, because my ballance accuracy is > about 0.77... i thought that I'm Doing maybe something wrong. > > > report: > > precision recall f1-score support > > 0.0 0.95 0.91 0.93 537 > 1.0 0.49 0.63 0.55 73 > > avg / total 0.89 0.88 0.88 610 > > > > > ------------------------------------------------------------------------------ > > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >
------------------------------------------------------------------------------
_______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
