Verified70% confidenceFactTime unknown
An AUC=1 on the ROC curve indicates a perfect classifier, while AUC=0.5 is equivalent to random guessing.
4
Sources
70%
Confidence
Long-term
Relevance
6/1/2026
First Seen
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UnverifiedAUC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82% similarUnverifiedThe 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 1.0.82% 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81% similarUnverifiedROC曲线下面积（AUC-ROC）越接近1，分类器越优秀80% similarUnverifiedAUC can be obtained by numerically integrating the area under the ROC curve using the trapezoidal rule, or equivalently computed via the Mann-Whitney U statistic.76% similar
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