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, ...)

# 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().

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)

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