Unverified50% confidenceSolutionExact time
Recording negative samples where 'a recommended solution did not apply' is just as important as recording successes; otherwise the model will fall into statistical bias, mistaking high-frequency solutions for high-accuracy ones
1
Sources
50%
Confidence
Long-term
Relevance
9/29/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn scenarios with extreme class imbalance (such as fraud detection), looking solely at accuracy as an evaluation metric is completely meaningless.76% similarUnverified评估基准测试质量的关键指标是假阳性率和假阴性率，理想基准应将两者控制在较低水平75% similarUnverifiedNegative samples for contrastive learning fine-tuning should use hard negatives, such as positions in the same field but at different levels, rather than random negative samples.75% similarUnverifiedThe core property of a strictly proper scoring rule is that the expected score is maximized if and only if the forecaster reports the probability they truly believe, with common examples including log-likelihood loss and the Brier score.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/957909API
curl https://kongchang.com/api/v1/knowledge/claims/957909MCP
get_claim(id=957909)