一种名为跨审计预测（CAP）的新统计方法已被开发出来，以改进模型风险预测，特别是在二元分类任务中。传统的K折交叉验证虽然普遍，但在估计特定类别风险方面有时表现不佳。CAP通过结合一个跨审计步骤来估计子样本中的过度乐观，以及一个用于纠正样本量减少的投影步骤，从而实现对经验风险更无偏的估计。该方法在模拟和实际的乳腺癌检测应用中都显示出理论优势和良好的性能。 AI
影响 增强了模型风险评估的可靠性，这对于负责任地部署AI系统至关重要。
排序理由 该集群包含一篇详细介绍模型风险预测新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2607.02328v1 Announce Type: cross Abstract: For training-data-based model risk prediction, $K$-fold cross-validation~(CV) is widely used to mitigate the well-known over-optimism of the empirical risk and is often regarded as reliable. However, for binary classification via …
For training-data-based model risk prediction, $K$-fold cross-validation~(CV) is widely used to mitigate the well-known over-optimism of the empirical risk and is often regarded as reliable. However, for binary classification via empirical risk minimization, our numerical studies…