Unverified50% confidenceConceptExact time
An AUC below 0.5 after swapping names and definitions indicates that the model scores can still distinguish the classes but the direction is systematically reversed, which cannot be corrected by adjusting the threshold.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedNone of the 9 models could clearly outperform the "fixed guess of 31%" baseline, with all models' AUC confidence intervals containing 0.5.71% 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 1.0.69% similarUnverifiedThe value of AUC ranges from 0 to 1, and a higher value indicates stronger classification ability of the model.69% 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.67% similarUnverifiedCalibration means that when the model outputs 0.9, across a large number of similar samples about 90% are indeed the correct class; an uncalibrated model can have high AUC but its values cannot be used directly as probabilities.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1029241API
curl https://kongchang.com/api/v1/knowledge/claims/1029241MCP
get_claim(id=1029241)