Setup Grid Search Parameters — setup

Create a GridSearchParams object that can be passed to train.

Usage

setup_GridSearch(
  resampler_parameters = setup_Resampler(n_resamples = 5L, type = "KFold"),
  search_type = "exhaustive",
  randomize_p = NULL,
  metrics_aggregate_fn = "mean",
  metric = NULL,
  maximize = NULL,
  parallel_type = "future",
  n_workers = rtemis_workers
)

Arguments

resampler_parameters: ResamplerParameters set by setup_Resampler.
search_type: Character: "exhaustive" or "randomized". Type of grid search to use. Exhaustive search will try all combinations of parameters. Randomized will try a random sample of size randomize_p * N of total combinations
randomize_p: Float (0, 1): For search_type == "randomized", randomly test this proportion of combinations.
metrics_aggregate_fn: Character: Name of function to use to aggregate error metrics.
metric: Character: Metric to minimize or maximize.
maximize: Logical: If TRUE, maximize metric, otherwise minimize it.
parallel_type: Character: Parallel backend to use.
n_workers: Integer: Number of workers to use.

Value

A GridSearchParams object.

Author

EDG