Unverified50% confidenceFactExact time
AUC的概率意义是随机抽取一个正例和一个负例，模型对正例打分高于负例的概率，该特性使其对类别不平衡不敏感
1
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50%
Confidence
Long-term
Relevance
7/16/2026
First Seen
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Related Claims
UnverifiedAUC equals the probability that, when one positive sample and one negative sample are randomly drawn from all positive and negative samples respectively, the predicted score of the positive sample is higher than that of the negative sample.82% similarUnverifiedAUC值域为0.5到1.0，0.5代表随机猜测、1.0代表完美预测，不受样本类别不平衡影响75% similarUnverifiedAUC是衡量二分类模型综合性能的核心指标，0.5代表随机猜测，1.0代表完美分类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.65% similarVerifiedAn AUC=1 on the ROC curve indicates a perfect classifier, while AUC=0.5 is equivalent to random guessing.64% similar
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