Unverified50% confidenceFactExact time
AURC (Area Under Risk-Coverage Curve) is a metric for measuring the quality of selective prediction; a smaller area under the curve indicates that the model is better able to rank high-risk samples into the rejection region.
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10/30/2026
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Related Claims
UnverifiedAUC-PR (area under the precision-recall curve) is commonly used as a model evaluation metric on imbalanced datasets.75% similarUnverifiedAUC（Area Under Curve）是衡量分类模型好坏最常用的指标之一75% similarUnverifiedWhen the ratio of positive to negative samples is extreme, the area under the PR curve (AUPRC) is often more revealing of true performance than AUC.72% similarVerifiedIn highly imbalanced datasets (with a positive rate below 5%), the area under the precision-recall curve (AP) often reflects the model's ability to detect the minority class better than AUC does.69% similarUnverifiedAUC is a comprehensive evaluation metric for binary classification models, with a value range of 0.5 to 1.0; it is not affected by class imbalance, and in clinical prediction, an AUC of 0.7–0.8 has some reference value, 0.8–0.9 is considered good, and above 0.9 is considered excellent.68% similar
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