recipes: step_other – R documentation

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step_other

Collapse Some 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")
)

## S3 method for class 'step_other'
tidy(x, ...)

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 which variables that will potentially be reduced. 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`	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 it's less than one then factor levels whose rate of occurrence in the training set are below `threshold` will be "othered". If it's greater or equal to one then it's treated as a frequency and factor levels that occur less then `threshold` times will be "othered".
`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.recipe()`.
`skip`	A logical. Should the step be skipped when the recipe is baked by `bake.recipe()`? While all operations are baked when `prep.recipe()` 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.
`x`	A `step_other` object.

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.recipe()). 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.

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 columns that will be affected) and retained (the factor levels that were not pulled into "other")

Examples

library(modeldata)
data(okc)

set.seed(19)
in_train <- sample(1:nrow(okc), size = 30000)

okc_tr <- okc[ in_train,]
okc_te <- okc[-in_train,]

rec <- recipe(~ diet + location, data = okc_tr)


rec <- rec %>%
  step_other(diet, location, threshold = .1, other = "other values")
rec <- prep(rec, training = okc_tr)

collapsed <- bake(rec, okc_te)
table(okc_te$diet, collapsed$diet, useNA = "always")

tidy(rec, number = 1)

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

# threshold as a frequency
rec <- recipe(~ diet + location, data = okc_tr)

rec <- rec %>%
  step_other(diet, location, threshold = 2000, other = "other values")
rec <- prep(rec, training = okc_tr)

tidy(rec, number = 1)
# compare it to
# okc_tr %>% count(diet, sort = TRUE) %>% top_n(4)
# okc_tr %>% count(location, sort = TRUE) %>% top_n(3)

recipes

Preprocessing Tools to Create Design Matrices

v0.1.16

MIT + file LICENSE

Authors

Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph]

Initial release