Feature selection step using the CAR score algorithm — step_select

`step_select_carscore` creates a *specification* of a recipe step that selects a subset of predictors as part of a regression model based on the scores of the CAR score algorithm. This step requires the `care` package to be installed. The top `top_p` scoring features, or features whose scores occur in the top percentile `threshold` will be retained as new predictors.

Usage

step_select_carscore(
  recipe,
  ...,
  outcome = NULL,
  role = NA,
  trained = FALSE,
  top_p = NA,
  threshold = NA,
  cutoff = NA,
  lambda = NA,
  diagonal = FALSE,
  exclude = NULL,
  scores = NULL,
  skip = FALSE,
  id = recipes::rand_id("select_carscore")
)

# S3 method for class 'step_select_carscore'
tidy(x, type = "terms", ...)

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 which predictors are affected by the step. See [selections()] for more details. For the `tidy` method, these are not currently used.
outcome: A character string with the name of the response variable. This must refer to a numeric feature for regression.
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.
top_p: An integer that will be used to select the `top_p` predictors with the smallest p-values. A value of `NA` implies that this criterion will be ignored.
threshold: A numeric value between 0 and 1 representing the percentile of best scoring features to select. For example `threshold = 0.9` will retain only predictors with scores in the top 90th percentile and a smaller threshold will select more features. Note that `top_p` and `threshold` are mutually exclusive but either can be used in conjunction with `cutoff` to select the top-ranked features and those that are smaller than the cutoff value.
cutoff: A numeric value, in -log10(p-value) units, where predictors with _larger_ than the cutoff will be retained. A value of `NA` implies that this criterion will be ignored.
lambda: The correlation shrinkage intensity (range 0-1).
diagonal: For diagonal = FALSE (the default) CAR scores are computed; otherwise with diagonal = TRUE marginal correlations.
exclude: A character vector of predictor names that will be removed from the data. This will be set when `prep()` is used on the recipe and should not be set by the user.
scores: A tibble with 'variable' and 'scores' columns containing the names of the variables and the absolute values of the calculated CAR scores. 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.recipe()? While all operations are baked when prep.recipe() 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_carscore` object.
type: A character with either 'terms' (the default) to return a tibble containing the variables that have been removed by the filter step, or 'scores' to return the scores for each variable.

Value

An updated version of `recipe` with the new step added to the sequence of existing steps (if any). For the `tidy` method, a tibble with a `terms` column for which predictors were removed.

Details

The recipe will stop if all of `top_p`, `threshold` and `cutoff` are left unspecified.

Examples

library(recipes)

data(car_prices, package = "modeldata")

rec <-
 recipe(Price ~ ., data = car_prices) %>%
 step_select_carscore(
   all_predictors(),
   outcome = "Price",
   top_p = 5,
   cutoff = 0.7
 )

prepped <- prep(rec)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.025 

new_data <- bake(prepped, new_data = NULL)
prepped
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:    1
#> predictor: 17
#> 
#> ── Training information 
#> Training data contained 804 data points and no incomplete rows.
#> 
#> ── Operations 
#> Carscore feature selection (12 excluded)