Plot variable importance

# S3 method for variable_importance
plot(
  x,
  title = "model",
  max_char = 40,
  caption = NULL,
  font_size = 11,
  point_size = 3,
  print = TRUE,
  ...
)

Arguments

x	A data frame from `get_variable_importance`
title	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 title.
max_char	Maximum length of variable names to leave untruncated. Default = 40; use `Inf` to prevent truncation. Variable names longer than this will be truncated to leave the beginning and end of each variable name, bridged by " ... ".
caption	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.
#> 
#> After data processing, models are being trained on 12 features with 50 observations.
#> Based on n_folds = 5 and hyperparameter settings, the following number of models will be trained: 5 rf's, 5 xgb's, and 50 glm's 
#> Training at fixed values: Random Forest
#> Training at fixed values: eXtreme Gradient Boosting
#> Training at fixed values: glmnet
#> 
#> *** 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. ***