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Fit the final best model to the training set and evaluate the test set

Source: R/last_fit.R
last_fit.Rd

last_fit() emulates the process where, after determining the best model, the final fit on the entire training set is needed and is then evaluated on the test set.

Usage

last_fit(object, ...)

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

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

Arguments

object

A parsnip model specification or a workflows::workflow(). No tuning parameters are allowed.

...

Currently unused.

preprocessor

A traditional model formula or a recipe created using recipes::recipe().

split

An rsplit object created from rsample::initial_split().

metrics

A yardstick::metric_set(), or NULL to compute a standard set of metrics.

control

A control_last_fit() object used to fine tune the last fit process.

Value

A single row tibble that emulates the structure of fit_resamples(). However, a list column called .workflow is also attached with the fitted model (and recipe, if any) that used the training set.

Details

This function is intended to be used after fitting a variety of models and the final tuning parameters (if any) have been finalized. The next step would be to fit using the entire training set and verify performance using the test data.

Examples

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>