Hi Luca, The AUC score is 1 as soon as all the samples with label 0 have a score less than the minimum score of the samples with label 1. Hope this helps Nicolas On 8 Sep 2015 5:10 pm, "Luca Puggini" <lucapug...@gmail.com> wrote:
> Hi, > I have a doubt regarding the AUC score. > > I would say that AUC should be 1 only if all the samples in class 0 have > score 0 and all the samples in class 1 have score 1. > > With the roc_auc_score function I get the value 1 for separable classes. > Isn't this wrong? Or maybe I am confused? > > x = np.arange(0, 1, .1) > y = np.array([0] * 7 + [1] * 3) > roc_auc_score(y, x) # = 1 > > In this example if I classify 1 x>.3 than I do not have a 0 error. So I > think that auc should not be 1. > > Let me know. > thanks, > Luca > -- > > Sent by mobile phone > > > ------------------------------------------------------------------------------ > > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >
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