Is it possible to understand if a classifier is overfitted just by looking at its ROC curve? I see that if its AUC it's too high (example 98%) is likely to be overfitted, but it can also mean that the classifier is just really good. Is there a way to tell these two cases apart?
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Since this is not a programming question, it is more appropriate for [CrossValidated](http://stats.stackexchange.com) – C8H10N4O2 Apr 21 '16 at 01:35
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Short answer: no, you can't.
Long answer: to estimate overfitting you need to assess your model on an independent dataset. Or use cross-validation, or something similar.

Calimo
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