论文标题
贝叶斯添加剂回归树的可视化
Visualizations for Bayesian Additive Regression Trees
论文作者
论文摘要
基于树的回归和分类已成为现代数据科学的标准工具。贝叶斯添加剂回归树（BART）尤其在处理相互作用和非线性效果方面灵活地获得了广泛的知名度。 Bart是一种基于贝叶斯树的机器学习方法，与其他预测模型相比，可以应用于回归和分类问题，并产生竞争或优越的结果。作为贝叶斯模型，巴特允许从业者通过后验分布探索预测的不确定性。在本文中，我们提出了用于探索巴特模型的新可视化技术。我们构建常规图来分析模型的性能和稳定性，并创建新的基于树的图以分析可变的重要性，相互作用和树结构。我们采用抑制价值的不确定性调色板（VSUP）来构建热图，以使用色等级共同表现出可变的重要性和相互作用，以表示后部不确定性。我们的新可视化旨在与最受欢迎的Bart R软件包合作，即Bart，Dbarts和Bartmachine。我们的方法是在B套件BARTMAN（BART模型分析）中实现的。
Tree-based regression and classification has become a standard tool in modern data science. Bayesian Additive Regression Trees (BART) has in particular gained wide popularity due its flexibility in dealing with interactions and non-linear effects. BART is a Bayesian tree-based machine learning method that can be applied to both regression and classification problems and yields competitive or superior results when compared to other predictive models. As a Bayesian model, BART allows the practitioner to explore the uncertainty around predictions through the posterior distribution. In this paper, we present new visualization techniques for exploring BART models. We construct conventional plots to analyze a model's performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using color scale to represent posterior uncertainty. Our new visualizations are designed to work with the most popular BART R packages available, namely BART, dbarts, and bartMachine. Our approach is implemented in the R package bartMan (BART Model ANalysis).
