Unverified50% confidenceSolutionExact time
置换检验通过大量随机置换(通常1000到10000次)构建零假设下的经验分布来估计p值,无需参数假设但计算成本相对较高
1
Sources
50%
Confidence
Long-term
Relevance
7/13/2026
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
Unverified在机器学习多种子实验中重复次数通常只有5到10次,样本量极小,正态性假设难以验证,因此 Wilcoxon 检验往往是更保守稳健的选择73% similarUnverified对于概率为10⁻⁶甚至更低的极端事件,蒙特卡洛方法要在随机采样中捕捉到一次,理论上需要百万至十亿次以上的独立模拟73% similarUnverified在8个模型均随机猜测的假设下,单次二元预测中至少有一个押中的概率约99.6%73% similarUnverifiedWhen a team tests 1,000 combinations of strategy parameters, even if all combinations have zero predictive power, roughly 50 combinations will appear to perform excellently in backtesting purely due to randomness (the multiple comparisons problem).72% similarUnverified在5%显著性水平下测试100个随机因子,期望会有5个看起来显著(多重假设检验问题)71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/502764API
curl https://kongchang.com/api/v1/knowledge/claims/502764MCP
get_claim(id=502764)