# Does it make sense to build a ROC for a decision tree where there are multiple threshold you can adjust?

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?

Thanks