Unverified85% confidenceFactTime unknown
AUC（Area Under Curve）是衡量分类模型好坏最常用的指标之一
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85%
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Long-term
Relevance
6/1/2026
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UnverifiedThe core advantage of AUC-ROC is its insensitivity to class imbalance; it reflects the model's overall discriminative ability across different classification thresholds, with AUC values ranging from 0.5 to 1.0.68% 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.68% 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% similarUnverifiedThe value of AUC ranges from 0 to 1, and a higher value indicates stronger classification ability of the model.65% similarUnverifiedIn 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.64% similar
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