step_other: Collapse infrequent categorical levels

Description

step_other() creates a specification of a recipe step that will potentially pool infrequently occurring values into an "other" category.

Usage

step_other(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  threshold = 0.05,
  other = "other",
  objects = NULL,
  skip = FALSE,
  id = rand_id("other")
)

Value

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

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.
threshold: A numeric value between 0 and 1, or an integer greater or equal to one. If less than one, then factor levels with a rate of occurrence in the training set below threshold will be pooled to other. If greater or equal to one, then this value is treated as a frequency and factor levels that occur less than threshold times will be pooled to other.
other: A single character value for the "other" category.
objects: A list of objects that contain the information to pool infrequent levels that is determined by prep().
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.

Tidying

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

terms: character, the selectors or variables selected
retained: character, factor levels not pulled into "other"
id: character, id of this step

Tuning Parameters

This step has 1 tuning parameters:

threshold: Threshold (type: double, default: 0.05)

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.

Details

The overall proportion (or total counts) of the categories are computed. The "other" category is used in place of any categorical levels whose individual proportion (or frequency) in the training set is less than threshold.

If no pooling is done the data are unmodified (although character data may be changed to factors based on the value of strings_as_factors in prep()). Otherwise, a factor is always returned with different factor levels.

If threshold is less than the largest category proportion, all levels except for the most frequent are collapsed to the other level.

If the retained categories include the value of other, an error is thrown. If other is in the list of discarded levels, no error occurs.

If no pooling is done, novel factor levels are converted to missing. If pooling is needed, they will be placed into the other category.

When data to be processed contains novel levels (i.e., not contained in the training set), the other category is assigned.

Examples

Run this code

data(Sacramento, package = "modeldata")

set.seed(19)
in_train <- sample(1:nrow(Sacramento), size = 800)

sacr_tr <- Sacramento[in_train, ]
sacr_te <- Sacramento[-in_train, ]

rec <- recipe(~ city + zip, data = sacr_tr)


rec <- rec %>%
  step_other(city, zip, threshold = .1, other = "other values")
rec <- prep(rec, training = sacr_tr)

collapsed <- bake(rec, sacr_te)
table(sacr_te$city, collapsed$city, useNA = "always")

tidy(rec, number = 1)

# novel levels are also "othered"
tahiti <- Sacramento[1, ]
tahiti$zip <- "a magical place"
bake(rec, tahiti)

# threshold as a frequency
rec <- recipe(~ city + zip, data = sacr_tr)

rec <- rec %>%
  step_other(city, zip, threshold = 2000, other = "other values")
rec <- prep(rec, training = sacr_tr)

tidy(rec, number = 1)
# compare it to
# sacr_tr %>% count(city, sort = TRUE) %>% top_n(4)
# sacr_tr %>% count(zip, sort = TRUE) %>% top_n(3)

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