将单调BART扩展到二分类结果，提升预测精度
Probit Monotone BART
- 基于probit模型构建单调BART框架
- 在二分类数据上实现单调均值函数估计
- 适合需保证单调性的二分类建模任务
Bayesian Additive Regression Trees（BART）由Chipman等人（2010）提出，已被证明是非参数建模与预测的强大工具。Monotone BART（Chipman等，2022）是近期进展，使BART在估计单调函数时更精确。本文进一步发展该框架，提出probit monotone BART，使单调BART能够在因变量为二分类时估计条件均值函数。
原文摘要 · Abstract (English)
Bayesian Additive Regression Trees (BART) of Chipman et al. (2010) has proven to be a powerful tool for nonparametric modeling and prediction. Monotone BART (Chipman et al., 2022) is a recent development that allows BART to be more precise in estimating monotonic functions. We further these developments by proposing probit monotone BART, which allows the monotone BART framework to estimate conditional mean functions when the outcome variable is binary.
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