Plot regularized model coefficients

# S3 method for interpret
plot(x, include_intercept = FALSE, max_char = 40, title,
  caption, font_size = 11, point_size = 3, print = TRUE, ...)



A interpret object or a data frame with columns "variable" and "coefficient"


If FALSE (default) the intercept estimate will not be plotted


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 " ... ".


Plot title. NULL for no title; character for custom title. If left blank contains the model class and outcome variable


Plot caption, appears in lower-right. NULL for no caption; character for custom caption. If left blank the caption will contain info including the hyperparameter values of the model used by interpret to determine coefficient estimates.


Relative size of all fonts in plot, default = 11


Size of dots, default = 3


Print the plot? Default = TRUE




A ggplot object, invisibly.

See also


machine_learn(mtcars, outcome = mpg, models = "glm", tune = FALSE) %>% interpret() %>% plot(font_size = 14)
#> Training new data prep recipe...
#> #> mpg looks numeric, so training regression algorithms.
#> #> After data processing, models are being trained on 10 features with 32 observations. #> Based on n_folds = 5 and hyperparameter settings, the following number of models will be trained: 50 glm's
#> 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. ***