step_select_cv function

Feature selection step using the coefficient of variation

Select variables with highest coefficient of variation.

step_select_cv(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  n_kept = NULL,
  prop_kept = NULL,
  cutoff = NULL,
  res = NULL,
  skip = FALSE,
  id = rand_id("select_cv")
)

## S3 method for class 'step_select_cv'
tidy(x, ...)

Arguments

• recipe: A recipe object. The step will be added to the sequence of operations for this recipe.
• ...: One or more selector functions to choose variables for this step. See selections() for more details.
• role: Not used by this step since no new variables are created.
• trained: A logical to indicate if the quantities for preprocessing have been estimated.
• n_kept: Number of variables to keep.
• prop_kept: A numeric value between 0 and 1 representing the proportion of variables to keep. n_kept and prop_kept are mutually exclusive.
• cutoff: Threshold beyond which (below or above) the variables are discarded.
• res: This parameter is only produced after the recipe has been trained.
• skip: A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
• id: A character string that is unique to this step to identify it.
• x: A step_select_cv object.

Returns

An updated version of recipe with the new step added to the sequence of any existing operations.

Examples

rec <-
  recipe(Species ~ ., data = iris) %>%
  step_select_cv(all_numeric_predictors(), n_kept = 2) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)

Author(s)

Antoine Bichat