Control aspects of the Bayesian search process

control_bayes(
  verbose = FALSE,
  no_improve = 10L,
  uncertain = Inf,
  seed = sample.int(10^5, 1),
  extract = NULL,
  save_pred = FALSE,
  time_limit = NA,
  pkgs = NULL
)

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.

no_improve

The integer cutoff for the number of iterations without better results.

uncertain

The number of iterations with no improvement before an uncertainty sample is created where a sample with high predicted variance is chosen (i.e., in a region that has not yet been explored). The iteration counter is reset after each uncertainty sample. For example, if uncertain = 10, this condition is triggered every 10 samples with no improvement.

seed

An integer for controlling the random number stream.

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.

time_limit

A number for the minimum number of minutes (elapsed) that the function should execute. The elapsed time is evaluated at internal checkpoints and, if over time, the results at that time are returned (with a warning). This means that the time_limit is not an exact limit, but a minimum time limit.

pkgs

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

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.