论文标题
正则化和虚假警报量化：解释性硬币的两个方面
Regularization and False Alarms Quantification: Two Sides of the Explainability Coin
论文作者
论文摘要
正则化是机器学习（ML）的公认技术，以实现最佳的偏见差异权衡，从而降低了模型的复杂性并增强了解释性。为此，必须对某些超参数进行调整，从而使ML模型准确地拟合看不见的数据以及可见的数据。在本文中，作者认为，超参数的正规化和误报的成本和误报风险的量化实际上是同一硬币的两个方面，即说明性。任何数量的不正确或不存在的估计都会破坏使用ML的经济价值的可衡量性，这可能使其实际上无用。
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.
