Unverified60% confidenceFactExact time
AUC 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.
2
Sources
60%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
VerifiedThe 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 1.0.81% 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81% similarVerifiedThe value of AUC ranges from 0 to 1, and a higher value indicates stronger classification ability of the model.80% similarVerifiedAccording to common industry standards, AUC > 0.9 is excellent, 0.8–0.9 is good, 0.7–0.8 is fair, 0.5–0.7 is poor, and 0.5 is equivalent to random guessing.78% similarVerifiedAUC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76% similar
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