For problems with binary classification, roc auc curve or roc auc score is often used to rate a model. But does the ROC ACC make sense in the context of a binary classification model that outputs only 0 or 1 for each observation (so no probability between [0,1] but only 0, 1)
1 Answer
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If the classifier, at its final step, doesn't produce a probability, and only makes $0,1$ predictions; ROC doesn't make sense because in order to draw a meaningful curve you need to sweep a threshold value. If all the values are either $0$ or $1$, sweeping the threshold makes no changes doesn't change the assigned classes, and has the same TPR, FPR for all the intermediary thresholds.