正则化与虚警量化：可解释性硬币的两面
Regularization and False Alarms Quantification: Two Sides of the Explainability Coin
摘要：
正则化是机器学习（ML）中一种成熟的技术，可以实现最佳的偏差-方差权衡，从而降低模型复杂性并增强可解释性。为此，必须调整一些超参数，使ML模型能够准确地拟合看不见的数据以及看到的数据。在这篇文章中，作者认为，超参数的正则化和虚警成本和风险的量化实际上是同一枚硬币的两面，可解释性。对这两个数量的不正确或不存在的估计破坏了使用ML的经济价值的可测量性，以至于可能使其实际上毫无用处。
英文摘要：
Regularization is a well-established technique in machine learning (ML) to achieve an optimal bias-variance trade-off which in turn reduces model complexity and enhances explainability. To this end, some hyper-parameters must be tuned, enabling the ML model to accurately fit the unseen data as well as the seen data. In this article, the authors argue that the regularization of hyper-parameters and quantification of costs and risks of false alarms are in reality two sides of the same coin, explainability. Incorrect or non-existent estimation of either quantities undermines the measurability of the economic value of using ML, to the extent that might make it practically useless.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
