Unverified50% confidenceFactExact time
Cluster-robust standard errors are used to handle cases where samples are not independent: when multiple records correspond to the same case, computing variance as if samples were independent underestimates the standard errors and inflates significance, and the correct approach is to aggregate by cluster first and then adjust the variance estimate.
1
Sources
50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Related Entities
Related Claims
Verified若用含测试集的全量数据计算均值和标准差，会导致预处理泄漏，使评估指标虚高，无法真实反映模型泛化能力80% similarUnverifiedIn class-imbalanced scenarios, accuracy alone can be misleading and needs to be combined with a confusion matrix for more fine-grained evaluation.77% similarUnverifiedIn multi-class tasks, accuracy becomes distorted when class samples are imbalanced; precision reflects the degree of false positives, recall reflects the degree of false negatives, and the F1 score is the harmonic mean of precision and recall.77% similarUnverifiedUnder label imbalance, accuracy is highly misleading: a model that always predicts the majority class can score very high on accuracy while missing every positive example.76% similarUnverifiedBias introduced by a single defective data point can leak into seemingly unrelated capability dimensions, causing the model to simultaneously regress across multiple independent metrics such as deceptiveness, compliance, and risk aversion.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1083200API
curl https://kongchang.com/api/v1/knowledge/claims/1083200MCP
get_claim(id=1083200)