Unverified50% confidenceFactExact time
The Brier score formula is BS = (1/N)Σ(f_i - o_i)^2, measuring the mean squared error between predicted probabilities and actual outcomes, where a lower score indicates greater accuracy and a perfect prediction scores 0.
1
Sources
50%
Confidence
Long-term
Relevance
9/28/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe Brier score assesses calibration quality by calculating the square of the difference between the predicted probability and the actual outcome, with a lower score indicating more accurate predictions78% similarUnverified贝叶斯最优分类器在0-1损失下等价于选择后验概率最大的类别，回归问题中条件期望在平方损失下是最优预测64% similarVerifiedExpected Calibration Error (ECE) bins prediction samples by confidence level, computes the weighted average of the difference between predicted confidence and actual accuracy within each bin, and ideally approaches 0.63% similarUnverifiedUnder the assumption that the true mapping is uniformly random, the paper proves a lower bound on the error rate of E ≥ (M/N)δ*(B/M) + (1-M/N)(1-1/K).63% similarUnverifiedThe load balancing loss uses the sum of squares of all expert selection frequencies L=ΣFi², and by the Cauchy–Schwarz inequality the loss is minimized when all Fi=1/N.62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/956114API
curl https://kongchang.com/api/v1/knowledge/claims/956114MCP
get_claim(id=956114)