15.4 Finalizing the model

Now we can finalize our choice of model.

wflow_id_best <- 
  res_ranks %>% 
  slice_min(rank, with_ties = FALSE) %>% 
  pull(wflow_id)

wf_best <-
  res_grid %>% 
  extract_workflow_set_result(wflow_id_best) %>% 
  select_best(metric = 'rmse')

fit_best <-
  res_grid %>% 
  extract_workflow(wflow_id_best) %>% 
  finalize_workflow(wf_best) %>% 
  last_fit(split = split)

metrics_best <-
  fit_best %>% 
  collect_metrics()
metrics_best

## # A tibble: 2 × 4
##   .metric .estimator .estimate .config             
##   <chr>   <chr>          <dbl> <chr>               
## 1 rmse    standard       0.514 Preprocessor1_Model1
## 2 rsq     standard       0.816 Preprocessor1_Model1

Finally, the canonical observed vs. predicted scatter plot.

p_preds <-
  fit_best %>% 
  collect_predictions() %>% 
  ggplot() +
  aes(x = !!col_y_sym, y = .pred) +
  geom_abline(linetype = 2) +
  # Big cuz we don't have that many points.
  geom_point(size = 4) +
  tune::coord_obs_pred() +
  labs(x = 'observed', y = 'predicted')
p_preds