Unverified50% confidenceEventExact time
A learner, when detecting forged receipts on this dataset, found that regardless of how the model was adjusted, the ROC-AUC consistently hovered between 0.46 and 0.54, close to random guessing.
1
Sources
50%
Confidence
Long-term
Relevance
10/9/2026
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.79% 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74% 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 1.0.74% similarUnverifiedWhen all three models are transferred to the LIAR benchmark, they all drop to near-random ranking levels (ROC-AUC of only 0.54–0.57), with none able to beat the majority-class baseline.74% similarVerifiedAUC equals the probability that, when one positive sample and one negative sample are randomly drawn from all positive and negative samples respectively, the predicted score of the positive sample is higher than that of the negative sample.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/990929API
curl https://kongchang.com/api/v1/knowledge/claims/990929MCP
get_claim(id=990929)