scimo (version 0.0.3)

step_select_cv: Feature selection step using the coefficient of variation

Description

Select variables with highest coefficient of variation.

Usage

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 step_select_cv
tidy(x, ...)

Value

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

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 recipes::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 recipes::bake()? While all operations are baked when recipes::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.

Author

Antoine Bichat

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)