Variable importance, interaction measures and partial dependence plots
are important summaries in the interpretation of statistical and machine
learning models. In our R package vivid (variable importance and
variable interaction displays) we create new visualisation 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 also construct 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 visualisations 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 and the interaction
structure complex. A practical example of the package in use can be
found here:
https://alaninglis.github.io/vivid/articles/vividVignette.html
The zenplots package (which is used within vivid) requires the
graph package from BioConductor. To install the graph and
zenplots packages use:
if (!requireNamespace("graph", quietly = TRUE)){
install.packages("BiocManager")
BiocManager::install("graph")
}
install.packages("zenplots")You can install the released version of vivid from CRAN with:
install.packages("vivid")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("AlanInglis/vivid")You can then load the package with:
library(vivid)