step_lencode: Likelihood encoding using analytical formula

In embed: Extra Recipes for Encoding Predictors

Likelihood encoding using analytical formula

Description

step_lencode() creates a specification of a recipe step that will convert a nominal (i.e. factor) predictor into a single set of scores derived analytically.

Usage

step_lencode(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  outcome = NULL,
  smooth = TRUE,
  mapping = NULL,
  skip = FALSE,
  id = rand_id("lencode")
)

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 step_lencode(), this indicates the variables to be encoded into a numeric format. See recipes::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	A logical to indicate if the quantities for preprocessing have been estimated.
outcome	A call to vars to specify which variable is used as the outcome. Only numeric and two-level factors are currently supported.
smooth	A logical, default to TRUE, should the estimates of groups with low counts be pulled towards the gobal estimate? Defaults to TRUE. See Details for how this is done. This is also known as partial pooling or shrinkage. Only works for numeric outcomes.
mapping	A list of tibble results that define the encoding. This is NULL until the step is trained by recipes::prep().
skip	A logical. Should the step be skipped when the recipe is baked by recipes::bake()? While all operations are baked when recipes::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.

Details

Each selected nominal predictor will be replaced by a numeric predictor. Each unique value of the nominal predictor is replaced by a numeric value. Thse values are calculated differently depending on the type of the outcome.

For numeric outcomes each value is the average value of the outcome inside each of the levels of the predictor. Unseen levels of the predictor will be using the global mean of the predictor. If case weights are used then a weighted mean is calculated instead.

For nominal outcomes each value is the log odds of the of the first level of the outcome variable being present, within each level of the levels of the predictor. Unseen levels will be replaced by the global log odds without stratification. If case weights are used then a weighted log odds is calculated.

If no or all occurances happens then the log odds is calculated using p = (2 * nrow(data) - 1) / (2 * nrow(data)) to avoid infinity that would happen by taking the log of 0.

For numeric outcomes where smooth = TRUE, the following adjustment is done.

estimate = (n / global_{var}) / (n / global_{var} + 1 / outcome_{var}) * estimate + (1 / outcome_{var}) / (n / global_{var} + 1 / outcome_{var}) * global_{mean}

Where n is the number of observations in the group.

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 for encoding), level (the factor levels), and value (the encodings).

Tidying

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

level

character, the factor levels

value

numeric, the encoding

terms

character, the selectors or variables selected

id

character, id of this step

Case weights

This step performs an supervised operation that can utilize case weights. To use them, see the documentation in recipes::case_weights and the examples on tidymodels.org.

References

Micci-Barreca D (2001) "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems," ACM SIGKDD Explorations Newsletter, 3(1), 27-32.

Zumel N and Mount J (2017) "vtreat: a data.frame Processor for Predictive Modeling," arXiv:1611.09477

Examples

library(recipes)
library(dplyr)
library(modeldata)

data(grants)

set.seed(1)
grants_other <- sample_n(grants_other, 500)
reencoded <- recipe(class ~ sponsor_code, data = grants_other) |>
  step_lencode(sponsor_code, outcome = vars(class), smooth = FALSE) |>
  prep()

bake(reencoded, grants_other)

tidy(reencoded, 1)

embed documentation built on Sept. 9, 2025, 5:28 p.m.