区分可改进与不可变的预测误差，让看似无法预测的问题变得更可解。
On the Unknowable Limits to Prediction
- 拆解预测误差，区分随机性与知识不足带来的误差
- 改进数据和算法后，预测准确率可逐步提升至极限
- 适合关注模型可解释性与长期优化的研究者
我们提出一种严格的预测误差分解方法，指出并非所有'不可约'误差都真正不可改变。许多领域可通过迭代提升测量精度、概念有效性与建模能力获益。该方法表明，看似'不可预测'的结果在数据（目标变量与特征）质量提升及算法优化后，可变得更加可处理。通过区分偶然误差（aleatoric）与认知误差（epistemic），我们厘清了预测准确率可渐近提升的路径——尽管内在随机性可能仍存在——并为计算研究提供了一个稳健的推进框架。
原文摘要 · Abstract (English)
We propose a rigorous decomposition of predictive error, highlighting that not all 'irreducible' error is genuinely immutable. Many domains stand to benefit from iterative enhancements in measurement, construct validity, and modeling. Our approach demonstrates how apparently 'unpredictable' outcomes can become more tractable with improved data (across both target and features) and refined algorithms. By distinguishing aleatoric from epistemic error, we delineate how accuracy may asymptotically improve--though inherent stochasticity may remain--and offer a robust framework for advancing computational research.
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