ประเมินผล

# Set up a tune grid to explore your model performance against roc_auc
lr_tune_output <- ___(lr_workflow, resamples = vfold_cv(train, v = 5),
  metrics = metric_set(roc_auc), grid = lr_penalty_grid)

# Select the best penalty value
best_penalty <- ___(lr_tune_output, metric = 'roc_auc', desc(penalty)) 

# Fit the final workflow with the best penalty
lr_final_fit<- ___(lr_workflow, best_penalty) %>% fit(data = train)

lr_final_fit %>% augment(test) %>% 
class_evaluate(truth = Attrition, estimate = .pred_class, .pred_Yes)