control_grid: Control aspects of the grid search process

Description

Control aspects of the grid search process

Usage

control_grid(
  verbose = FALSE,
  allow_par = TRUE,
  extract = NULL,
  save_pred = FALSE,
  pkgs = NULL,
  save_workflow = FALSE
)
control_resamples(
  verbose = FALSE,
  allow_par = TRUE,
  extract = NULL,
  save_pred = FALSE,
  pkgs = NULL,
  save_workflow = FALSE
)

Arguments

verbose

A logical for logging results as they are generated. Despite this argument, warnings and errors are always shown. If using a dark IDE theme, some logging messages might be hard to see. If this is the case, try setting the tidymodels.dark option with options(tidymodels.dark = TRUE) to print lighter colors.

allow_par

A logical to allow parallel processing (if a parallel backend is registered).

extract

An optional function with at least one argument (or NULL) that can be used to retain arbitrary objects from the model fit object, recipe, or other elements of the workflow.

save_pred

A logical for whether the out-of-sample predictions should be saved for each model evaluated.

pkgs

An optional character string of R package names that should be loaded (by namespace) during parallel processing.

save_workflow

A logical for whether the workflow should be appended to the output as an attribute.

Details

For extract, this function can be used to output the model object, the recipe (if used), or some components of either or both. When evaluated, the function's sole argument has a fitted workflow If the formula method is used, the recipe element will be NULL.

The results of the extract function are added to a list column in the output called .extracts. Each element of this list is a tibble with tuning parameter column and a list column (also called .extracts) that contains the results of the function. If no extraction function is used, there is no .extracts column in the resulting object. See tune_bayes() for more specific details.

Note that for collect_predictions(), it is possible that each row of the original data point might be represented multiple times per tuning parameter. For example, if the bootstrap or repeated cross-validation are used, there will be multiple rows since the sample data point has been evaluated multiple times. This may cause issues when merging the predictions with the original data.

control_resamples() is an alias for control_grid() and is meant to be used with fit_resamples().