step_other {recipes} | R Documentation |
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")
)
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 |
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 |
other |
A single character value for the other category, default to
|
objects |
A list of objects that contain the information to pool
infrequent levels that is determined by |
skip |
A logical. Should the step be skipped when the recipe is baked by
|
id |
A character string that is unique to this step to identify it. |
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.
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
, 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
.
See Also
Other dummy variable and encoding steps:
step_bin2factor()
,
step_count()
,
step_date()
,
step_dummy()
,
step_dummy_extract()
,
step_dummy_multi_choice()
,
step_factor2string()
,
step_holiday()
,
step_indicate_na()
,
step_integer()
,
step_novel()
,
step_num2factor()
,
step_ordinalscore()
,
step_regex()
,
step_relevel()
,
step_string2factor()
,
step_time()
,
step_unknown()
,
step_unorder()
Examples
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)