Unceta Irene, Nin Jordi, Pujol Oriol
BBVA Data & Analytics, 28050 Madrid, Spain.
ESADE, Universitat Ramon Llull, 08172 Sant Cugat del Vallès, Spain.
Entropy (Basel). 2021 Mar 30;23(4):407. doi: 10.3390/e23040407.
Differential replication is a method to adapt existing machine learning solutions to the demands of highly regulated environments by reusing knowledge from one generation to the next. Copying is a technique that allows differential replication by projecting a given classifier onto a new hypothesis space, in circumstances where access to both the original solution and its training data is limited. The resulting model replicates the original decision behavior while displaying new features and characteristics. In this paper, we apply this approach to a use case in the context of credit scoring. We use a private residential mortgage default dataset. We show that differential replication through copying can be exploited to adapt a given solution to the changing demands of a constrained environment such as that of the financial market. In particular, we show how copying can be used to replicate the decision behavior not only of a model, but also of a full pipeline. As a result, we can ensure the decomposability of the attributes used to provide explanations for credit scoring models and reduce the time-to-market delivery of these solutions.
差异复制是一种通过将知识代代相传，使现有机器学习解决方案适应高度规范环境需求的方法。复制是一种技术，在原始解决方案及其训练数据的访问受限的情况下，通过将给定分类器投影到新的假设空间来实现差异复制。所得模型在显示新特征和特性的同时，复制了原始决策行为。在本文中，我们将此方法应用于信用评分背景下的一个用例。我们使用了一个私人住宅抵押贷款违约数据集。我们表明，通过复制进行差异复制可用于使给定解决方案适应诸如金融市场等受限环境不断变化的需求。特别是，我们展示了复制不仅可用于复制模型的决策行为，还可用于复制完整管道的决策行为。因此，我们可以确保用于为信用评分模型提供解释的属性的可分解性，并减少这些解决方案的上市交付时间。
