Partial dependence plot from workflow?

[Kylian]

I created the following recipe to predict my random forest:

set.seed(123456)
cv_folds <- Data_train %>% vfold_cv(v = 4, strata = Lead_week)

# Create a recipe
rf_mod_recipe <- recipe(Lead_week ~ Jaar + Aantal + Verzekering + Leeftijd + Retentie + 
                          Aantal_proeven + Geslacht + FLG_ADVERTISING + FLG_MAIL +
                          FLG_PHONE + FLG_EMAIL + Proef1 + Proef2 + Regio + 
                          Month + AC,
                        data = Data_train) %>%
                        step_normalize(Leeftijd) 

# Specify the recipe
rf_mod <- rand_forest(mtry = tune(), min_n = tune(), trees = 200) %>%
  set_mode("regression") %>%
  set_engine("ranger", importance = "permutation")

# Create a workflow
rf_mod_workflow <-  workflow() %>%
  add_model(rf_mod) %>%
  add_recipe(rf_mod_recipe) 
rf_mod_workflow

# State our error metrics
class_metrics <- metric_set(rmse, mae)

registerDoParallel()

rf_grid <- grid_regular(
  mtry(range = c(5, 15)),
  min_n(range = c(10, 200)),
  levels = 5
)

rf_grid

# Train the model
set.seed(654321)

rf_tune_res <- tune_grid(
  rf_mod_workflow,
  resamples = cv_folds,
  grid = rf_grid,
  metrics = class_metrics
)

# Collect the optimal hyperparameters
rf_tune_res %>%
  collect_metrics()

rf_tune_res %>%
  collect_metrics() %>%
  filter(.metric %in% c("rmse", "mae")) %>%
  ggplot(aes(x = mtry, y = mean, ymin = mean - std_err, ymax = mean + std_err, 
             colour = .metric)) +
  geom_errorbar() +
  geom_line() +
  geom_point() +
  facet_grid(.metric ~., scales = "free_y")

# Plot of the train results
rf_tune_res %>%
  collect_metrics() %>%
  filter(.metric %in% c("rmse")) %>%
  mutate(min_n = factor(min_n)) %>%
  ggplot(aes(x = mtry, y = mean, colour = min_n)) +
  geom_line() +
  geom_point() +
  labs(y = "error")

# Select the best number of mtry
best_rmse <- select_best(rf_tune_res, "rmse")
rf_final_wf <- finalize_workflow(rf_mod_workflow, best_rmse)
rf_final_wf

# Create a workflow
rf_mod_workflow <-  workflow() %>%
  add_model(rf_mod) %>%
  add_recipe(rf_mod_recipe) 
rf_mod_workflow

predict(rf_final_wf, grid) %>%
  bind_cols(rf_mod_recipe %>% select(AC)) %>%
  ggplot(aes(y = .pred, x = AC)) +
  geom_path()

Test set performance

# Finalise the workflow
set.seed(56789)
rf_final_fit <- rf_final_wf %>%
  last_fit(splits, metrics = class_metrics)

# Collect predictions
summary_rf <- rf_final_fit %>% 
  collect_predictions()

summary(summary_rf$.pred)

# Collect metrics
rf_final_fit %>% 
  collect_metrics()

So I used cross-validation to finetune and eventually test on holdout data. However, how do I get partial dependence plots to 'open the black box'?

[Max]

See this documentation from the DALEXtra package.