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What is AUC?

Data Science Interview Questions based on AUC.

Few weeks ago, I started wrote about ROC curves. The purpose was to provide a basic primer on ROC curves. As a follow up, this article talks about AUC.

Photo by Zbysiu Rodak on Unsplash

AUC stands for Area Under the Curve. ROC can be quantified using AUC. The way it is done is to see how much area has been covered by the ROC curve. If we obtain a perfect classifier, then the AUC score is 1.0. If the classifier is random in its guesses, then the AUC score is 0.5. In the real world, we don’t expect an AUC score of 1.0, but if the AUC score for the classifier is in the range of 0.6 to 0.9, then it is considered to be a good classifier.

AUC for the ROC curve

In the preceding figure, the area under the curve which has been covered becomes our AUC score. This gives us an indication of how good or bad our classifier is performing. ROC and AUC are the two indicators that can provide us with insights on how our classifier performs.

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Reference: ML Solutions


What is AUC? was originally published in Acing AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.