I understand building a ROC curve when the output is a probability, say, from a logistic regression model. You can build a ROC curve by varying the cutoff threshold.
But what about decision trees of the form:
if attribute_1 > x: decision = positive else: if attribute_2 < y: decision = position else: decision = negative
You can adjust the cutoff for both attributes and all will affect your confusion matrix. Does it make sense to build a ROC curves when there are multiple thresholds?