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

...  Currently unused. 
preprocessor  A traditional model formula or a recipe created using

split  An 
metrics  A 
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.
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.
# \donttest{ 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 #> # Monte Carlo crossvalidation (0.75/0.25) with 1 resamples #> # A tibble: 1 x 6 #> splits id .metrics .notes .predictions .workflow #> <list> <chr> <list> <list> <list> <list> #> 1 <split [24/… train/test s… <tibble [2 ×… <tibble [0 … <tibble [8 × … <workflo…# test set results spline_res$.metrics[[1]]#> # A tibble: 2 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 rmse standard 5.74 #> 2 rsq standard 0.503# 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 #> # Monte Carlo crossvalidation (0.75/0.25) with 1 resamples #> # A tibble: 1 x 6 #> splits id .metrics .notes .predictions .workflow #> <list> <chr> <list> <list> <list> <list> #> 1 <split [24/… train/test s… <tibble [2 ×… <tibble [0 … <tibble [8 × … <workflo…# }