yes thanks a lot. I was confused. Are you aware of any default metric to measure how well two classes are separated?
On Tue, Sep 8, 2015 at 6:27 PM Nicolas Goix <[email protected]> wrote: > 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" <[email protected]> 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 >> [email protected] >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > -- Sent by mobile phone
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