  We propose novel applications of parallel coordinates plots and Sankey
diagrams to represent the hierarchies of interacting covariate effects in
random forests. Each visualisation summarises the frequencies of all of the
paths through all of the trees in a random forest. Visualisations of the roles
of covariates in random forests include: ranked bar or dot charts depicting
scalar metrics of the contributions of individual covariates to the predictive
accuracy of the random forest; line graphs depicting various summaries of the
effect of varying a particular covariate on the predictions from the random
forest; heatmaps of metrics of the strengths of interactions between all pairs
of covariates; and parallel coordinates plots for each response class depicting
the distributions of the values of all covariates among the observations most
representative of those predicted to belong that class. Together these
visualisations facilitate substantial insights into the roles of covariates in
a random forest but do not communicate the frequencies of the hierarchies of
covariates effects across the random forest or the orders in which covariates
occur in these hierarchies. Our visualisations address these gaps. We
demonstrate our visualisations using a random forest fitted to publicly
available data and provide a software implementation in the form of an R
package.
