What is AUC (AU ROC)? When to use it?

AUC stands for Area Under the ROC Curve. ROC is a probability curve and AUC represents degree or measure of separability. It’s used when we need to value how much model is capable of distinguishing between classes. The value is between 0 and 1, the higher the better.

How to interpret the AU ROC score?

AUC score is the value of Area Under the ROC Curve.

If we assume ROC curve consists of dots, , then

An excellent model has AUC near to the 1 which means it has good measure of separability. A poor model has AUC near to the 0 which means it has worst measure of separability. When AUC score is 0.5, it means model has no class separation capacity whatsoever.

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