Mini toolsDefinition
The area under the ROC curve, providing a single score for model performance across all possible classification thresholds.
Detailed Explanation
AUC ranges from 0 to 1, where 1 represents perfect classification and 0.5 represents random guessing. It measures the entire two-dimensional area underneath the ROC curve, providing an aggregate measure of performance across all possible classification thresholds.
Use Cases
Used in credit scoring, disease diagnosis, and customer churn prediction where comparing different models' overall performance is crucial.
