论文标题
分类层次结构中模型的贝叶斯压力测试
Bayesian Stress Testing of Models in a Classification Hierarchy
论文作者
论文摘要
在现实生活中建立机器学习解决方案通常涉及将问题分解为各种复杂性的多种模型。这在整体绩效，更好的结果的解释性以及更轻松的模型维护方面具有优势。在这项工作中，我们提出了一个贝叶斯框架，以模拟这种层次结构中模型之间的相互作用。我们表明，该框架可以促进整体解决方案的压力测试，从而在积极部署之前对其预期性能有更多的信心。最后，我们在玩具问题和财务欺诈检测数据集上测试了提出的框架，以说明如何将其应用于基于机器学习的解决方案，而不论所需的基本建模如何。
Building a machine learning solution in real-life applications often involves the decomposition of the problem into multiple models of various complexity. This has advantages in terms of overall performance, better interpretability of the outcomes, and easier model maintenance. In this work we propose a Bayesian framework to model the interaction amongst models in such a hierarchy. We show that the framework can facilitate stress testing of the overall solution, giving more confidence in its expected performance prior to active deployment. Finally, we test the proposed framework on a toy problem and financial fraud detection dataset to demonstrate how it can be applied for any machine learning based solution, regardless of the underlying modelling required.
