step_discretize: Discretize Numeric Variables
In recipes: Preprocessing and Feature Engineering Steps for Modeling

step_discretize

R Documentation

Discretize Numeric Variables

Description

step_discretize() creates a specification of a recipe step that will convert numeric data into a factor with bins having approximately the same number of data points (based on a training set).

Usage

step_discretize(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  num_breaks = 4,
  min_unique = 10,
  objects = NULL,
  options = list(prefix = "bin"),
  skip = FALSE,
  id = rand_id("discretize")
)

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.
`num_breaks`	An integer defining how many cuts to make of the data.
`min_unique`	An integer defining a sample size line of dignity for the binning. If (the number of unique values)`⁠/(cuts+1)⁠` is less than `min_unique`, no discretization takes place.
`objects`	The `discretize()` objects are stored here once the recipe has be trained by `prep()`.
`options`	A list of options to `discretize()`. A default is set for the argument `x`. Note that using the options `prefix` and `labels` when more than one variable is being transformed might be problematic as all variables inherit those values.
`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.

Tidying

When you tidy() this step, a tibble is returned with columns terms, value , and id:

terms: character, the selectors or variables selected
value: numeric, the breaks
id: character, id of this step

Tuning Parameters

This step has 2 tuning parameters:

min_unique: Unique Value Threshold (type: integer, default: 10)
num_breaks: Number of Cut Points (type: integer, default: 4)

Case weights

The underlying operation does not allow for case weights.

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_discretize(carbon, hydrogen)

rec <- prep(rec, biomass_tr)
binned_te <- bake(rec, biomass_te)
table(binned_te$carbon)

tidy(rec, 1)

recipes documentation built on April 13, 2025, 5:10 p.m.