Slope usually means there are ties in your predictions. Check your dataset to see if you have repeated predicted values (possibly 1 or 0).
On Sat, Jan 7, 2017 at 4:32 PM, José Ismael Fernández Martínez < ismael...@ciencias.unam.mx> wrote: > But is not a scikit-learn classifier, is a keras classifier which, in the > functional API, predict returns probabilities. > What I don't understand is why my plot of the roc curve has a slope, since > I call roc_curve passing the actual label as y_true and the output of the > classifier (score probabilities) as y_score for every element tested. > > > > Sent from my iPhone > On Jan 7, 2017, at 4:04 PM, Joel Nothman <joel.noth...@gmail.com> wrote: > > predict method should not return probabilities in scikit-learn > classifiers. predict_proba should. > > On 8 January 2017 at 07:52, José Ismael Fernández Martínez < > ismael...@ciencias.unam.mx> wrote: > >> Hi, I have a multilabel classifier written in Keras from which I want to >> compute AUC and plot a ROC curve for every element classified from my test >> set. >> >> <image1.PNG> >> >> Everything seems fine, except that some elements have a roc curve that >> have a slope as follows: >> >> [image: enter image description here] >> <https://i.stack.imgur.com/XCNCA.png>I don't know how to interpret the >> slope in such cases. >> >> Basically my workflow goes as follows, I have a pre-trained model, >> instance of Keras, and I have the features X and the binarized labels y, >> every element in y is an array of length 1000, as it is a multilabel >> classification problem each element in y might contain many 1s, >> indicating that the element belongs to multiples classes, so I used the >> built-in loss of binary_crossentropy and my outputs of the model >> prediction are score probailities. Then I plot the roc curve as follows. >> >> >> The predict method returns probabilities, as I'm using the functional api >> of keras. >> >> Does anyone knows why my roc curves looks like this? >> >> >> Ismael >> >> >> Sent from my iPhone >> >> _______________________________________________ >> 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 > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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