What does ROC AUC mean in machine learning?

What does ROC AUC mean in machine learning?

ROC AUC stands for Receiver Operating Characteristic Area Under the Curve. It's a performance measurement for classification models.

How is ROC AUC calculated?

ROC AUC is calculated by plotting the true positive rate against the false positive rate at various threshold settings and measuring the area under the curve.

What does a ROC AUC score of 0.5 indicate?

A ROC AUC score of 0.5 indicates that the model has no discrimination ability, meaning it performs no better than random chance.

What is considered a good ROC AUC score?

Generally, a ROC AUC score above 0.7 is considered acceptable, above 0.8 is good, and above 0.9 is excellent.