How to predict the test set's confidence interval using a tuned model from tidymodels in R?

Question

I am fitting a random forest model using tidymodels in R, and an error occurs when I try to predict the test set using the tuned model: Each element of splits must be an rsplit object.

# Data splitting
data(Sacramento, package = "modeldata")
set.seed(123)
data_split <- initial_split(Sacramento, prop = 0.75, strata = price)
Sac_train <- training(data_split)
Sac_test <- testing(data_split)

# Build the model
rf_mod <- rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% 
          set_engine("ranger", importance = "permutation") %>% 
          set_mode("regression")

# Create the recipe
Sac_recipe <- recipe(price ~ ., data = Sac_train) %>% 
              step_rm(zip, latitude, longitude) %>% 
              step_corr(all_numeric_predictors(), threshold = 0.85) %>% 
              step_zv(all_numeric_predictors()) %>% 
              step_normalize(all_numeric_predictors()) %>%
              step_dummy(all_nominal_predictors())

# Create the workflow
rf_workflow <- workflow() %>% 
               add_model(rf_mod) %>% 
               add_recipe(Sac_recipe)

# Train and Tune the model
set.seed(123)
Sac_folds <- vfold_cv(Sac_train, v = 10, repeats = 2, strata = price)

rf_res <- rf_workflow %>% 
          tune_grid(grid = 2*2,
                    resamples = Sac_folds, 
                    control = control_grid(save_pred = TRUE),
                    metrics = metric_set(rmse))

# Extract the best model
rf_best <- rf_res %>%
           select_best(metric = "rmse")

# Last fit
last_rf_workflow <- rf_workflow %>% 
                    finalize_workflow(rf_best)

last_rf_fit <- last_rf_workflow %>% 
               last_fit(Sac_train)
# Error: Each element of `splits` must be an `rsplit` object.

predict(last_rf_fit, Sac_test, type = "conf_int")

Answer 1

The error generates from these lines,

last_rf_fit <- last_rf_workflow %>% 
               last_fit(Sac_train)

Now from the documentation of last_fit ,

# S3 method for workflow
last_fit(object, split, ..., metrics = NULL, control = control_last_fit())

So an workflow object is passed to last_fit as the first argument via %>% and Sac_train is passed to split parameter.

But from the docs, the split argument needs to be,

An rsplit object created from rsample::initial_split()

Instead, try this,

last_rf_fit <- last_rf_workflow %>% 
  last_fit(data_split)

Then to collect the predictions, following the docs ,

collect_predictions(last_rf_fit)