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The sklearn roc_curve docstring states:

"y_score : array, shape = [n_samples] Target scores, can either be probability estimates of the positive class, confidence values, or binary decisions."

In what situation it would make sense to set y_score to a binary vector ("binary decisions")? Wouldn't that result in a ROC curve with one point on it which kind of defies the point?

Chris Gorgolewski
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If you are using a classifier that does not output probability scores (e.g. svm.SVC without an explicit probability=True), there isn't a way to compute a ROC curve. As an API designer, you have two choices: raise an exception and provide the user no useful information, or plot a degenerate curve with one data point. I would argue the latter is more useful.

mbatchkarov
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    We actually had a student who generated those degenerate ROC curves, calculated AUC and thought everything is all right. I would lean towards raising an exception. – Chris Gorgolewski Feb 19 '14 at 12:05