# 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.