Journal of Data Science,
Statistics, and Visualisation

Visualisations for Bayesian Additive Regression Trees




Model visualisation, Bayesian Additive Regression Trees, posterior uncertainty, variable importance, uncertainty visualisation


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 Visualisation techniques for exploring BART models. We construct conventional plots to analyse a model’s performance and stability as well as create new tree-based plots to analyse variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our new Visualisations 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).


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How to Cite

Inglis, A., Parnell, A., & Hurley, C. (2024). Visualisations for Bayesian Additive Regression Trees. Journal of Data Science, Statistics, and Visualisation, 4(1).
Journal of Data Science,
Statistics, and Visualisation