Unverified50% confidenceFactExact time
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
1
Sources
50%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
McNemar检验对比机器学习模型：随机种子下的正确用法
redditr/learnmachinelearning7/11/2026
Related Claims
UnverifiedIn few-shot prompting, simply changing the order of examples can cause dramatic fluctuations in accuracy, ranging from near-random to near-optimal.80% similarUnverifiedLarge model outputs are stochastic (controlled by the Temperature parameter), so a single comparison is not conclusive and requires multiple sampling and statistical comparisons.74% similarUnverifiedA 2018 study by the Google Brain team showed that simply changing the random seed could cause performance fluctuations of several percentage points in certain models.72% similarUnverified深度学习中的随机种子控制权重初始化、数据加载批次打乱顺序、Dropout神经元随机失活以及优化器中的随机梯度估计等多个随机性来源72% similarUnverified即便固定所有随机种子，在不同硬件或并行度下，训练结果仍可能出现被大模型放大为显著能力差异的细微偏差72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/503210API
curl https://kongchang.com/api/v1/knowledge/claims/503210MCP
get_claim(id=503210)