Plot variable importance

# S3 method for variable_importance
plot(x, caption = "model", title = NULL,
  font_size = 11, point_size = 3, print = TRUE, ...)

Arguments

x

A data frame from get_variable_importance

caption

Either "model", "none", or a string to be used as the plot caption. "model" puts the name of the best-performing model, on which variable importances are generated, in the caption.

title

Plot title

font_size

Relative size for all fonts, default = 11

point_size

Size of dots, default = 3

print

Print the plot?

...

Unused

Value

A ggplot object, invisibly.

Examples

machine_learn(pima_diabetes[1:50, ], patient_id, outcome = diabetes, tune = FALSE) %>% get_variable_importance() %>% plot()
#> Training new data prep recipe
#> Variable(s) ignored in prep_data won't be used to tune models: patient_id
#> diabetes looks categorical, so training classification algorithms.
#> Training at fixed values: Random Forest
#> Training at fixed values: k-Nearest Neighbors
#> #> *** Models successfully trained. The model object contains the training data minus ignored ID columns. *** #> *** If there was PHI in training data, normal PHI protocols apply to the model object. ***