Interactive Graphics for Visually Diagnosing Forest Classifiers in R

TL;DR

This paper presents interactive R graphics tools for diagnosing and understanding forest classifiers, enabling detailed exploration of model complexity, variable importance, and uncertainty in ensemble models.

Contribution

It introduces interactive visualization methods in R for analyzing forest classifiers, enhancing interpretability of ensemble models like random forests and projection pursuit forests.

Findings

Interactive graphics reveal insights into class structure and variable importance.

Tools help assess model complexity and prediction uncertainty.

Applicable to various bagged ensemble methods.

Abstract

This paper describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an example of an ensemble, 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 are explored in this paper, 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 to the projection pursuit forest, but could be more broadly applied to other bagged ensembles. Interactive graphics are built in R, using the ggplot2, plotly, and shiny packages.