plot_multi_way_importance: Multi-way importance plot

In ModelOriented/randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable Importance

Multi-way importance plot

Description

Plot two or three measures of importance of variables in a random fores. Choose importance measures from the colnames(importance_frame).

Usage

plot_multi_way_importance(
  importance_frame,
  x_measure = "mean_min_depth",
  y_measure = "times_a_root",
  size_measure = NULL,
  min_no_of_trees = 0,
  no_of_labels = 10,
  main = "Multi-way importance plot"
)

Arguments

importance_frame	A result of using the function measure_importance() to a random forest or a randomForest object
x_measure	The measure of importance to be shown on the X axis
y_measure	The measure of importance to be shown on the Y axis
size_measure	The measure of importance to be shown as size of points (optional)
min_no_of_trees	The minimal number of trees in which a variable has to be used for splitting to be used for plotting
no_of_labels	The approximate number of best variables (according to all measures plotted) to be labeled (more will be labeled in case of ties)
main	A string to be used as title of the plot

Value

A ggplot object

Examples

forest <- randomForest::randomForest(Species ~ ., data = iris, localImp = TRUE)
plot_multi_way_importance(measure_importance(forest))

ModelOriented/randomForestExplainer documentation built on March 23, 2024, 10:31 p.m.