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: >> 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
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