R tune last_fit 將最終的最佳模型擬合到訓練集並評估測試集

last_fit(object, ...)

# S3 method for model_spec
last_fit(
  object,
  preprocessor,
  split,
  ...,
  metrics = NULL,
  control = control_last_fit(),
  add_validation_set = FALSE
)

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

library(recipes)
library(rsample)
library(parsnip)

set.seed(6735)
tr_te_split <- initial_split(mtcars)

spline_rec <- recipe(mpg ~ ., data = mtcars) %>%
  step_ns(disp)

lin_mod <- linear_reg() %>%
  set_engine("lm")

spline_res <- last_fit(lin_mod, spline_rec, split = tr_te_split)
spline_res
#> # Resampling results
#> # Manual resampling 
#> # A tibble: 1 × 6
#>   splits         id              .metrics .notes   .predictions .workflow 
#>   <list>         <chr>           <list>   <list>   <list>       <list>    
#> 1 <split [24/8]> train/test spl… <tibble> <tibble> <tibble>     <workflow>

# test set results
spline_res$.metrics[[1]]
#> # A tibble: 2 × 4
#>   .metric .estimator .estimate .config             
#>   <chr>   <chr>          <dbl> <chr>               
#> 1 rmse    standard       3.80  Preprocessor1_Model1
#> 2 rsq     standard       0.729 Preprocessor1_Model1

# or use a workflow

library(workflows)
spline_wfl <-
  workflow() %>%
  add_recipe(spline_rec) %>%
  add_model(lin_mod)

last_fit(spline_wfl, split = tr_te_split)
#> # Resampling results
#> # Manual resampling 
#> # A tibble: 1 × 6
#>   splits         id              .metrics .notes   .predictions .workflow 
#>   <list>         <chr>           <list>   <list>   <list>       <list>    
#> 1 <split [24/8]> train/test spl… <tibble> <tibble> <tibble>     <workflow>