step_best_normalize: Run bestNormalize transformation for `recipes` implementation

Description

`step_best_normalize` creates a specification of a recipe step (see `recipes` package) that will transform data using the best of a suite of normalization transformations estimated (by default) using cross-validation.

Usage

step_best_normalize(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  transform_info = NULL,
  transform_options = list(),
  num_unique = 5,
  skip = FALSE,
  id = rand_id("best_normalize")
)
# S3 method for step_best_normalize
tidy(x, ...)
# S3 method for step_best_normalize
axe_env(x, ...)

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 columns `terms` (the selectors or variables selected) and `value` (the lambda estimate).

Arguments

recipe: A formula or recipe
...: One or more selector functions to choose which variables are affected by the step. See [selections()] for more details. For the `tidy` method, these are not currently used.
role: Not used by this step since no new variables are created.
trained: For recipes functionality
transform_info: A numeric vector of transformation values. This (was transform_info) is `NULL` until computed by [prep.recipe()].
transform_options: options to be passed to bestNormalize
num_unique: An integer where data that have less possible values will not be evaluate for a transformation.
skip: For recipes functionality
id: For recipes functionality
x: A `step_best_normalize` object.

Details

The bestnormalize transformation can be used to rescale a variable to be more similar to a normal distribution. See `?bestNormalize` for more information; `step_best_normalize` is the implementation of `bestNormalize` in the `recipes` context.

As of version 1.7, the `butcher` package can be used to (hopefully) improve scalability of this function on bigger data sets.

Examples

Run this code


library(recipes)
rec <- recipe(~ ., data = as.data.frame(iris))

bn_trans <- step_best_normalize(rec, all_numeric())

bn_estimates <- prep(bn_trans, training = as.data.frame(iris))

bn_data <- bake(bn_estimates, as.data.frame(iris))

plot(density(iris[, "Petal.Length"]), main = "before")
plot(density(bn_data$Petal.Length), main = "after")

tidy(bn_trans, number = 1)
tidy(bn_estimates, number = 1)

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