CODEWITHSUNDEEP
machine-learningrocauc
amiref
amiref

Reputation: 3441

Measuring ROC and AUC

I have read plenty of articles about ROC and AUC, and I found out we need to measure TPR and FPR for different classification thresholds. Does it mean that ROC and AUC can be measured for only probabilistic classifiers and not the descrete ones (like trees)?

Upvotes: 0

Views: 386

Answers (1)

veritessa
veritessa

Reputation: 236

Yes, in order to calculate AUC, you need to have predicted probabilities. AUC is the area under the ROC curve. To make a ROC curve you need to calculate true positive rate and false positive rate for different decision thresholds - and in order to use different decision thresholds, you need to have probabilities as your model's output (because it makes no sense to apply a threshold to a binary label 0 or 1.) For more information about how to calculate AUC, when to use AUC, and the strengths and weakness of AUC as a performance metric, you can read this article.

Upvotes: 1

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