`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, ...)
# Default S3 method
fit_best(x, ...)
# S3 method for class 'tune_results'
fit_best(
x,
...,
metric = NULL,
eval_time = 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, must be empty.

- metric
A character string (or

`NULL`

) for which metric to optimize. If`NULL`

, the first metric is used.- eval_time
A single numeric time point where dynamic event time metrics should be chosen (e.g., the time-dependent ROC curve, etc). The values should be consistent with the values used to create

`x`

. The`NULL`

default will automatically use the first evaluation time used by`x`

.- 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, eval_time)`

. If not`NULL`

,`parameters`

overwrites the specification via`metric`

, and`eval_time`

.- 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()`

.

## 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)
```

## Case Weights

Some models can utilize case weights during training. tidymodels currently supports two types of case weights: importance weights (doubles) and frequency weights (integers). Frequency weights are used during model fitting and evaluation, whereas importance weights are only used during fitting.

To know if your model is capable of using case weights, create a model spec
and test it using `parsnip::case_weights_allowed()`

.

To use them, you will need a numeric column in your data set that has been
passed through either `hardhat:: importance_weights()`

or
`hardhat::frequency_weights()`

.

For functions such as `fit_resamples()`

and the `tune_*()`

functions, the
model must be contained inside of a `workflows::workflow()`

. To declare that
case weights are used, invoke `workflows::add_case_weights()`

with the
corresponding (unquoted) column name.

From there, the packages will appropriately handle the weights during model fitting and (if appropriate) performance estimation.

## See also

`last_fit()`

is closely related to `fit_best()`

. They both
give you access to a workflow fitted on the training data but are situated
somewhat differently in the modeling workflow. `fit_best()`

picks up
after a tuning function like `tune_grid()`

to take you from tuning results
to fitted workflow, ready for you to predict and assess further. `last_fit()`

assumes you have made your choice of hyperparameters and finalized your
workflow to then take you from finalized workflow to fitted workflow and
further to performance assessment on the test data. While `fit_best()`

gives
a fitted workflow, `last_fit()`

gives you the performance results. If you
want the fitted workflow, you can extract it from the result of `last_fit()`

via extract_workflow().

## 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: 7
#> 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 15.3
#> 4 13.3
#> 5 19.5
#> 6 21.1
#> 7 19.9
#> 8 18.7
#> 9 19.6
#> 10 17.9
#> # ℹ 44 more rows
```