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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

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The ROC Curve has no relation with the way your model works, but instead, with its outputs. If the target is binary and your model outputs anything in between 0 to 1 (e.g. [0, 0.2, 0.4, ..., 1] or continuous probabilities), there's a sense in building the ROC Curve. If instead, the only outputs of your model are 0 or 1, the ROC Curve would be kind of useless, and computing simpler metrics like Precision and Recall would make more sense.

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