  Variable importance, interaction measures, and partial dependence plots are
important summaries in the interpretation of statistical and machine learning
models. In this paper we describe new visualization techniques for exploring
these model summaries. We construct heatmap and graph-based displays showing
variable importance and interaction jointly, which are carefully designed to
highlight important aspects of the fit. We describe a new matrix-type layout
showing all single and bivariate partial dependence plots, and an alternative
layout based on graph Eulerians focusing on key subsets. Our new visualizations
are model-agnostic and are applicable to regression and classification
supervised learning settings. They enhance interpretation even in situations
where the number of variables is large. Our R package vivid (variable
importance and variable interaction displays) provides an implementation.
