Unverified90% confidenceFactTime unknown
iPSAE评分对Binder二分类预测的AUC仅为0.65，远低于可靠分类器水平
1
Sources
90%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedWhen the ratio of positive to negative samples is extreme, the area under the PR curve (AUPRC) is often more revealing of true performance than AUC.69% similarUnverifiedIn highly imbalanced datasets (with a positive rate below 5%), the area under the precision-recall curve (AP) often reflects the model's ability to detect the minority class better than AUC does.68% 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.68% 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 1.0.67% similarUnverifiedMIT斯隆管理学院2022年研究发现不同ESG评级机构对同一企业评分相关性仅为0.54，远低于信用评级机构约0.9264% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/21651API
curl https://kongchang.com/api/v1/knowledge/claims/21651MCP
get_claim(id=21651)