  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.
