AUROC
A common metric for evaluating the ranking ability of binary classifiers, ranging from 0.5 to 1.0, representing the probability that a positive sample receives a higher score than a randomly chosen negative sample. It is often used in monitoring scenarios to rank failed and successful trajectories.
Core Facts
Timeline (last 90 days)
Research on cross-model verification shows that the AUROC for blind spot detection is 0.70, while same-model self-checking is only 0.59.
S3KG achieves a peak AUROC of 0.973.
Different cohort construction approaches mean that even if two studies report similar AUROCs, they do not necessarily address the same clinical problem, and a model from one institution cannot be directly deployed at another.
AUC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
All Facts (5)
AUC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%UnverifiedResearch on cross-model verification shows that the AUROC for blind spot detection is 0.70, while same-model self-checking is only 0.59.
50%UnverifiedS3KG achieves a peak AUROC of 0.973.
50%UnverifiedDifferent cohort construction approaches mean that even if two studies report similar AUROCs, they do not necessarily address the same clinical problem, and a model from one institution cannot be directly deployed at another.
50%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
50%