Area under the ROC curve.
Measures the ability of a model to distinguish between classes by plotting the true positive rate (recall) against the false positive rate (FPR) at various thresholds (imbalanced datasets).
Assesses model’s ability to distinguish between classes.
- Range: Between 0 and 1, where 1 indicates perfect #ml/classification and 0.5 suggests no discriminative power (equivalent to random guessing).
#Advantages
- Robust to class imbalance
- provides a comprehensive view of performance across all classification thresholds.
Almost Correct Prediction Error Rate