Fit a model to the numerically optimal configuration

fit_best() takes the results from model tuning and fits it to the training set using tuning parameters associated with the best performance.

Usage

fit_best(x, ...)

# S3 method for default
fit_best(x, ...)

# S3 method for tune_results
fit_best(
  x,
  metric = NULL,
  parameters = NULL,
  verbose = FALSE,
  add_validation_set = NULL,
  ...
)

Arguments

x: The results of class tune_results (coming from functions such as tune_grid(), tune_bayes(), etc). The control option save_workflow = TRUE should have been used.
...: Not currently used.
metric: A character string (or NULL) for which metric to optimize. If NULL, the first metric is used.
parameters: An optional 1-row tibble of tuning parameter settings, with a column for each tuning parameter. This tibble should have columns for each tuning parameter identifier (e.g. "my_param" if tune("my_param") was used). If NULL, this argument will be set to select_best(metric).
verbose: A logical for printing logging.
add_validation_set: When the resamples embedded in x are a split into training set and validation set, should the validation set be included in the data set used to train the model? If not, only the training set is used. If NULL, the validation set is not used for resamples originating from rsample::validation_set() while it is used for resamples originating from rsample::validation_split().

Value

A fitted workflow.

Details

This function is a shortcut for the manual steps of:


  best_param <- select_best(tune_results, metric) # or other `select_*()`
  wflow <- finalize_workflow(wflow, best_param)  # or just `finalize_model()`
  wflow_fit <- fit(wflow, data_set)

In comparison to last_fit(), that function requires a finalized model, fits the model on the training set defined by rsample::initial_split(), and computes metrics from the test set.

Examples

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

data(meats, package = "modeldata")
meats <- meats %>% select(-water, -fat)

set.seed(1)
meat_split <- initial_split(meats)
meat_train <- training(meat_split)
meat_test  <- testing(meat_split)

set.seed(2)
meat_rs <- vfold_cv(meat_train, v = 10)

pca_rec <-
  recipe(protein ~ ., data = meat_train) %>%
  step_normalize(all_numeric_predictors()) %>%
  step_pca(all_numeric_predictors(), num_comp = tune())

knn_mod <- nearest_neighbor(neighbors = tune()) %>% set_mode("regression")

ctrl <- control_grid(save_workflow = TRUE)

set.seed(128)
knn_pca_res <-
  tune_grid(knn_mod, pca_rec, resamples = meat_rs, grid = 10, control = ctrl)

knn_fit <- fit_best(knn_pca_res, verbose = TRUE)
#> Using rmse as the metric, the optimal parameters were:
#>   neighbors: 6
#>   num_comp:  4
#> 
#> ℹ Fitting using 161 data points...
#> ✔ Done.
predict(knn_fit, meat_test)
#> # A tibble: 54 × 1
#>    .pred
#>    <dbl>
#>  1  19.7
#>  2  20.1
#>  3  15.0
#>  4  13.2
#>  5  19.6
#>  6  21.1
#>  7  19.9
#>  8  18.5
#>  9  19.6
#> 10  17.9
#> # ℹ 44 more rows