Plotting decision tree results from tidymodels

Question

I have managed to build a decision tree model using the tidymodels package but I am unsure how to pull the results and plot the tree. I know I can use the rpart and rpart.plot packages to achieve the same thing but I would rather use tidymodels as that is what I am learning. Below is an example using the mtcars data.

library(tidymodels)
library(rpart)
library(rpart.plot)
library(dplyr) #contains mtcars

#data
df <- mtcars %>%
    mutate(gear = factor(gear))


#train/test
set.seed(1234)

df_split <- initial_split(df)
df_train <- training(df_split)
df_test <- testing(df_split)


df_recipe <- recipe(gear~ ., data = df) %>%
  step_normalize(all_numeric())


#building model
tree <- decision_tree() %>%
   set_engine("rpart") %>%
   set_mode("classification")

#workflow
 tree_wf <- workflow() %>%
   add_recipe(df_recipe) %>%
   add_model(tree) %>%
   fit(df_train) #results are found here 

rpart.plot(tree_wf$fit$fit) #error is here

The error I get says Error in rpart.plot(tree_wf$fit$fit): Not an rpart object which makes sense but I am unaware if there is a package or step I am missing to convert the results into a format that rpart.plot will allow me to plot. This might not be possible but any help would be much appreciated.

Answer 1

You can also use the workflows::pull_workflow_fit() function. It makes the code a little bit more elegant.

tree_fit <- tree_wf %>% 
  pull_workflow_fit()
rpart.plot(tree_fit$fit)

Answer 2

The following works (note the extra $fit ):

rpart.plot(tree_wf$fit$fit$fit)

Not a very elegant solution, but it does plot the tree.

Tested with parsnip 0.1.3 and rpart.plot 3.0.8.