Verified65% confidenceFactTime unknown
AUC 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.
3
Sources
65%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedAn AUC=1 on the ROC curve indicates a perfect classifier, while AUC=0.5 is equivalent to random guessing.76% similarUnverifiedROC曲线下面积（AUC-ROC）越接近1，分类器越优秀75% 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 1.0.75% similarVerifiedThe value of AUC ranges from 0 to 1, and a higher value indicates stronger classification ability of the model.71% similarUnverifiedAUC-PR (area under the precision-recall curve) is commonly used as a model evaluation metric on imbalanced datasets.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/20874API
curl https://kongchang.com/api/v1/knowledge/claims/20874MCP
get_claim(id=20874)