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Feature selection step using the coefficient of variation

Source: R/select_cv.R
step_select_cv.Rd

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, ...)

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.

Value

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

Author

Antoine Bichat

Examples

rec <-
  recipe(Species ~ ., data = iris) %>%
  step_select_cv(all_numeric_predictors(), n_kept = 2) %>%
  prep()
rec
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 4
#> 
#> ── Training information 
#> Training data contained 150 data points and no incomplete rows.
#> 
#> ── Operations 
#> • Top CV filtering on: Sepal.Length and Sepal.Width, ... | Trained
tidy(rec, 1)
#> # A tibble: 4 × 4
#>   terms           cv kept  id             
#>   <chr>        <dbl> <lgl> <chr>          
#> 1 Sepal.Length 0.142 FALSE select_cv_9QeuR
#> 2 Sepal.Width  0.143 FALSE select_cv_9QeuR
#> 3 Petal.Length 0.470 TRUE  select_cv_9QeuR
#> 4 Petal.Width  0.636 TRUE  select_cv_9QeuR
bake(rec, new_data = NULL)
#> # A tibble: 150 × 3
#>    Petal.Length Petal.Width Species
#>           <dbl>       <dbl> <fct>  
#>  1          1.4         0.2 setosa 
#>  2          1.4         0.2 setosa 
#>  3          1.3         0.2 setosa 
#>  4          1.5         0.2 setosa 
#>  5          1.4         0.2 setosa 
#>  6          1.7         0.4 setosa 
#>  7          1.4         0.3 setosa 
#>  8          1.5         0.2 setosa 
#>  9          1.4         0.2 setosa 
#> 10          1.5         0.1 setosa 
#> # ℹ 140 more rows