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Feature selection step using background level

Source: R/select_background.R
step_select_background.Rd

Select features that exceed a background level in at least a defined number of samples.

Usage

step_select_background(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  background_level = NULL,
  n_samples = NULL,
  prop_samples = NULL,
  res = NULL,
  skip = FALSE,
  id = rand_id("select_background")
)

# S3 method for step_select_background
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.

background_level

Background level to exceed.

n_samples, prop_samples

Count or proportion of samples in which a feature exceeds background_level to be retained.

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_background object.

Value

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

Author

Antoine Bichat

Examples

rec <-
  iris %>%
  recipe(formula = Species ~ .) %>%
  step_select_background(all_numeric_predictors(),
                         background_level = 4, prop_samples = 0.5) %>%
  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 
#> • Background filtering on: Sepal.Length and Sepal.Width, ... | Trained
tidy(rec, 1)
#> # A tibble: 4 × 3
#>   terms        kept  id                     
#>   <chr>        <lgl> <chr>                  
#> 1 Sepal.Length TRUE  select_background_mb92Q
#> 2 Sepal.Width  FALSE select_background_mb92Q
#> 3 Petal.Length TRUE  select_background_mb92Q
#> 4 Petal.Width  FALSE select_background_mb92Q
bake(rec, new_data = NULL)
#> # A tibble: 150 × 3
#>    Sepal.Length Petal.Length Species
#>           <dbl>        <dbl> <fct>  
#>  1          5.1          1.4 setosa 
#>  2          4.9          1.4 setosa 
#>  3          4.7          1.3 setosa 
#>  4          4.6          1.5 setosa 
#>  5          5            1.4 setosa 
#>  6          5.4          1.7 setosa 
#>  7          4.6          1.4 setosa 
#>  8          5            1.5 setosa 
#>  9          4.4          1.4 setosa 
#> 10          4.9          1.5 setosa 
#> # ℹ 140 more rows