[scikit-learn] Roc curve from multilabel classification has slope

[José Ismael Fernández Martínez]

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.



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


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