Unverified95% confidenceFactTime unknown
AUC的取值范围在0到1之间，数值越大代表模型分类能力越强
1
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95%
Confidence
Long-term
Relevance
6/1/2026
First Seen
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Related Claims
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 1.0.84% 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.80% 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79% 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% similarVerifiedAn AUC=1 on the ROC curve indicates a perfect classifier, while AUC=0.5 is equivalent to random guessing.74% similar
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