step_percentile: Percentile Transformation
In recipes: Preprocessing and Feature Engineering Steps for Modeling

step_percentile

R Documentation

Percentile Transformation

Description

step_percentile() creates a specification of a recipe step that replaces the value of a variable with its percentile from the training set.

Usage

step_percentile(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  ref_dist = NULL,
  options = list(probs = (0:100)/100),
  outside = "none",
  skip = FALSE,
  id = rand_id("percentile")
)

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`	For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
`trained`	A logical to indicate if the quantities for preprocessing have been estimated.
`ref_dist`	The computed percentiles is stored here once this preprocessing step has be trained by `prep()`.
`options`	A named list of options to pass to `stats::quantile()`. See Details for more information.
`outside`	A character, describing how interpolation is to take place outside the interval `⁠[min(x), max(x)]⁠`. `none` means nothing will happen and values outside the range will be `NA`. `lower` means that new values less than `min(x)` will be given the value `0`. `upper` means that new values larger than `max(x)` will be given the value `1`. `both` will handle both cases. Defaults to `none`.
`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.

Value

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

Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

Examples


data(biomass, package = "modeldata")

biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]

rec <- recipe(
  HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
  data = biomass_tr
) %>%
  step_percentile(carbon)

prepped_rec <- prep(rec)

prepped_rec %>%
  bake(biomass_te)

tidy(rec, 1)
tidy(prepped_rec, 1)

recipes documentation built on Aug. 26, 2023, 1:08 a.m.