Unverified50% confidenceOpinionExact time
Replacing binary matching with confidence scores did not eliminate brittleness; it merely shifted the brittleness from the decision boundary to the calibration stage.
1
Sources
50%
Confidence
Long-term
Relevance
8/4/2026
First Seen
Sources
置信度评分vs二元规则匹配:AI系统该如何选择
redditr/LanguageTechnology8/3/2026
Related Claims
Unverified为几个百分点的榜单差距进行定向优化是本末倒置,评估模型时不应盲目迷信榜单排名69% similarUnverifiedThe optimal regularization strength needs to be selected through cross-validation; if it is too large it leads to underfitting, and if it is too small the regularization effect is negligible.67% similarUnverifiedDietterich (1998) pointed out that even when using cross-validation, the folds are not completely independent of one another because there is substantial overlap between training sets, which may cause inflation of the false positive rate.67% similarUnverifiedBenchmark saturation refers to the point at which, after model performance improves to a certain level, benchmark tests can no longer effectively differentiate capability differences between models.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/685444API
curl https://kongchang.com/api/v1/knowledge/claims/685444MCP
get_claim(id=685444)