Boilerplate Workflow

This is a boilerplate function to create automatically the following:

• recipe

• model specification

• workflow

• tuned model (grid ect)

Usage

hai_auto_cubist(
  .data,
  .rec_obj,
  .splits_obj = NULL,
  .rsamp_obj = NULL,
  .tune = TRUE,
  .grid_size = 10,
  .num_cores = 1,
  .best_metric = "rmse"
)

Arguments

.data

The data being passed to the function. The time-series object.

.rec_obj

This is the recipe object you want to use. You can use hai_cubist_data_prepper() an automatic recipe_object.

.splits_obj

NULL is the default, when NULL then one will be created.

.rsamp_obj

NULL is the default, when NULL then one will be created. It will default to creating an rsample::mc_cv() object.

.tune

Default is TRUE, this will create a tuning grid and tuned workflow

.grid_size

Default is 10

.num_cores

Default is 1

.best_metric

Default is "rmse". The only .model_type you can use with Cubist is regression so use hai_default_regression_metric_set() to get the available metrics. Because of this the .model_type parameter is omitted from this function.

Value

A list

Details

This uses the parsnip::cubist_rules() with the engine set to cubist

See also

Other Boiler_Plate: hai_auto_c50(), hai_auto_earth(), hai_auto_glmnet(), hai_auto_knn(), hai_auto_ranger(), hai_auto_svm_poly(), hai_auto_svm_rbf(), hai_auto_wflw_metrics(), hai_auto_xgboost()

Other cubist: hai_cubist_data_prepper()

Author

Steven P. Sanderson II, MPH

Examples

if (FALSE) { # \dontrun{
data <- mtcars

rec_obj <- hai_cubist_data_prepper(data, mpg ~ .)

auto_cube <- hai_auto_cubist(
  .data = data,
  .rec_obj = rec_obj,
  .best_metric = "rmse"
)

auto_cube$recipe_info
} # }