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step_other creates a specification of a recipe
step that will potentially pool infrequently occurring values
into an "other" category.
step_other(
recipe,
...,
role = NA,
trained = FALSE,
threshold = 0.05,
other = "other",
objects = NULL,
skip = FALSE,
id = rand_id("other")
)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.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
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.
A single character value for the "other" category.
A list of objects that contain the information
to pool infrequent levels that is determined by
prep().
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.
A character string that is unique to this step to identify it.
An updated version of recipe with the new step added to the
sequence of any existing operations.
When you tidy() this step, a tibble with columns
terms (the columns that will be affected) and retained (the factor
levels that were not pulled into "other") is returned.
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.
Other dummy variable and encoding steps:
step_bin2factor(),
step_count(),
step_date(),
step_dummy_extract(),
step_dummy_multi_choice(),
step_dummy(),
step_factor2string(),
step_holiday(),
step_indicate_na(),
step_integer(),
step_novel(),
step_num2factor(),
step_ordinalscore(),
step_regex(),
step_relevel(),
step_string2factor(),
step_unknown(),
step_unorder()
# NOT RUN {
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)
# }
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