The Additive Voronoi Tessellations (AddiVortes) model is a multivariate regression model that uses multiple Voronoi tessellations to partition the covariate space for an additive ensemble model. In this paper, the AddiVortes framework is extended to binary classification by incorporating a probit model with a latent variable formulation. Specifically, we utilise a data augmentation technique, where a latent variable is introduced and the binary response is determined via thresholding. In most cases, the AddiVortes model outperforms random forests, BART and other leading black-box regression models when compared using a range of metrics. A comprehensive analysis is conducted using AddiVortes to predict an individual's likelihood of being approved for a home mortgage, based on a range of covariates. This evaluation highlights the model's effectiveness in capturing complex relationships within the data and its potential for improving decision-making in mortgage approval processes.

翻译：加性沃罗诺伊镶嵌（AddiVortes）模型是一种多元回归模型，它利用多个沃罗诺伊镶嵌对协变量空间进行划分，以构建一个加性集成模型。本文通过引入潜变量公式并结合概率单位模型，将AddiVortes框架扩展至二元分类问题。具体而言，我们采用了一种数据增强技术，即引入一个潜变量，并通过阈值化方法确定二元响应。在大多数情况下，当使用一系列指标进行比较时，AddiVortes模型的表现优于随机森林、BART以及其他领先的黑箱回归模型。本文基于一系列协变量，使用AddiVortes对个人获得住房抵押贷款批准的可能性进行了全面分析。该评估突显了该模型在捕捉数据中复杂关系方面的有效性，及其在改进抵押贷款审批流程决策方面的潜力。