Interactive graphics for visually diagnosing forest classifiers in R

Natalia da Silva (), Dianne Cook () and Eun-Kyung Lee ()
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Natalia da Silva: Universidad de la República
Dianne Cook: Monash University
Eun-Kyung Lee: Ewha Womans University

Computational Statistics, 2025, vol. 40, issue 6, No 11, 3105-3125

Abstract: Abstract This article describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an example of an ensemble since it is produced by bagging multiple trees. The process of bagging and combining results from multiple trees produces numerous diagnostics which, with interactive graphics, can provide a lot of insight into class structure in high dimensions. Various aspects of models are explored in this article, to assess model complexity, individual model contributions, variable importance and dimension reduction, and uncertainty in prediction associated with individual observations. The ideas are applied to the random forest algorithm and projection pursuit forest but could be more broadly applied to other bagged ensembles helping in the interpretability deficit of these methods. Interactive graphics are built in R using the ggplot2, plotly, and shiny packages.

Keywords: Statistical visualization; Interactive visualization; Interpretable machine learning; Ensemble model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-023-01323-x

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