Unverified85% confidenceTradeoffExact time
On class-imbalanced datasets, Macro F1 is extremely sensitive to rare class performance, where small threshold changes cause dramatic fluctuations in precision and recall for rare classes
1
Sources
85%
Confidence
Long-term
Relevance
8/2/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn class-imbalanced scenarios, accuracy alone can be misleading and needs to be combined with a confusion matrix for more fine-grained evaluation.62% similarUnverified权重初始化的随机性对模型性能影响最深远,数据批次打乱顺序的随机性影响最小61% similarUnverified温度超参数τ越小分布越集中,模型对难负样本越敏感,但τ过小会导致梯度对异常值敏感、训练不稳定61% similarUnverifiedLong-tail distribution refers to a data distribution characteristic where a small number of categories account for a large number of samples while a large number of categories have very few samples, causing the model to overfit head categories and achieve very low prediction accuracy on tail categories.60% similarUnverified量化的核心挑战在于处理权重分布中的异常值,LLM权重中少量极大值会显著放大量化误差59% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/680250API
curl https://kongchang.com/api/v1/knowledge/claims/680250MCP
get_claim(id=680250)