Area Under the Receiver Operating Characteristic Curve
AUC-ROC is the acronym for Area Under the Receiver Operating Characteristic Curve.
A metric used to evaluate the performance of a binary classification model. It quantifies the model’s ability to distinguish between the two classes across all possible classification thresholds.
The ROC curve is a plot that illustrates the true positive rate (sensitivity) against the false positive rate (1-specificity) for different classification thresholds. The true positive rate (TPR) is the proportion of actual positive cases that are correctly identified by the model, while the false positive rate (FPR) is the proportion of actual negative cases that are incorrectly identified as positive.
The AUC-ROC is the area under this ROC curve. It ranges from 0 to 1, where a higher value indicates better model performance. An AUC-ROC of 1 represents a perfect classifier that can distinguish between the two classes without error, while an AUC-ROC of 0.5 indicates that the model performs no better than random chance. In practice, an AUC-ROC value closer to 1 is desirable, as it demonstrates the model’s ability to accurately classify both positive and negative cases.
- Abbreviation: AUC-ROC