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Control aspects of the grid search racing process


  verbose = FALSE,
  verbose_elim = FALSE,
  allow_par = TRUE,
  extract = NULL,
  save_pred = FALSE,
  burn_in = 3,
  num_ties = 10,
  alpha = 0.05,
  randomize = TRUE,
  pkgs = NULL,
  save_workflow = FALSE,
  event_level = "first",
  parallel_over = "everything",
  backend_options = NULL



A logical for logging results (other than warnings and errors, which are always shown) as they are generated during training in a single R process. When using most parallel backends, this argument typically will not result in any logging. If using a dark IDE theme, some logging messages might be hard to see; try setting the tidymodels.dark option with options(tidymodels.dark = TRUE) to print lighter colors.


A logical for whether logging of the elimination of tuning parameter combinations should occur.


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


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.


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


An integer for how many resamples should be completed for all grid combinations before parameter filtering begins.


An integer for when tie-breaking should occur. If there are two final parameter combinations being evaluated, num_ties specified how many more resampling iterations should be evaluated. After num_ties more iterations, the parameter combination with the current best results is retained.


The alpha level for a one-sided confidence interval for each parameter combination.


Should the resamples be evaluated in a random order? By default, the resamples are evaluated in a random order so the random number seed should be control prior to calling this method (to be reproducible). For repeated cross-validation the randomization occurs within each repeat.


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


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


A single string containing either "first" or "second". This argument is passed on to yardstick metric functions when any type of class prediction is made, and specifies which level of the outcome is considered the "event".


A single string containing either "resamples" or "everything" describing how to use parallel processing. Alternatively, NULL is allowed, which chooses between "resamples" and "everything" automatically.

If "resamples", then tuning will be performed in parallel over resamples alone. Within each resample, the preprocessor (i.e. recipe or formula) is processed once, and is then reused across all models that need to be fit.

If "everything", then tuning will be performed in parallel at two levels. An outer parallel loop will iterate over resamples. Additionally, an inner parallel loop will iterate over all unique combinations of preprocessor and model tuning parameters for that specific resample. This will result in the preprocessor being re-processed multiple times, but can be faster if that processing is extremely fast.

If NULL, chooses "resamples" if there are more than one resample, otherwise chooses "everything" to attempt to maximize core utilization.

Note that switching between parallel_over strategies is not guaranteed to use the same random number generation schemes. However, re-tuning a model using the same parallel_over strategy is guaranteed to be reproducible between runs.


An object of class "tune_backend_options" as created by tune::new_backend_options(), used to pass arguments to specific tuning backend. Defaults to NULL for default backend options.


An object of class control_race that echos the argument values.


#> Racing method control object