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
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
thresholdwill be pooled toother. If greater or equal to one, then this value is treated as a frequency and factor levels that occur less thanthresholdtimes will be pooled toother.- 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 whenprep()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 usingskip = TRUEas it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.
Value
An updated version of recipe with the new step added to the
sequence of any existing operations.
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.
Tidying
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.
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_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_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")
#>
#> ELK_GROVE SACRAMENTO other values <NA>
#> ANTELOPE 0 0 3 0
#> AUBURN 0 0 0 0
#> CAMERON_PARK 0 0 1 0
#> CARMICHAEL 0 0 2 0
#> CITRUS_HEIGHTS 0 0 6 0
#> COOL 0 0 0 0
#> DIAMOND_SPRINGS 0 0 1 0
#> EL_DORADO 0 0 1 0
#> EL_DORADO_HILLS 0 0 4 0
#> ELK_GROVE 16 0 0 0
#> ELVERTA 0 0 1 0
#> FAIR_OAKS 0 0 0 0
#> FOLSOM 0 0 3 0
#> FORESTHILL 0 0 0 0
#> GALT 0 0 2 0
#> GARDEN_VALLEY 0 0 0 0
#> GOLD_RIVER 0 0 1 0
#> GRANITE_BAY 0 0 0 0
#> GREENWOOD 0 0 0 0
#> LINCOLN 0 0 1 0
#> LOOMIS 0 0 0 0
#> MATHER 0 0 0 0
#> MEADOW_VISTA 0 0 0 0
#> NORTH_HIGHLANDS 0 0 4 0
#> ORANGEVALE 0 0 1 0
#> PENRYN 0 0 0 0
#> PLACERVILLE 0 0 1 0
#> POLLOCK_PINES 0 0 0 0
#> RANCHO_CORDOVA 0 0 1 0
#> RANCHO_MURIETA 0 0 1 0
#> RIO_LINDA 0 0 0 0
#> ROCKLIN 0 0 2 0
#> ROSEVILLE 0 0 9 0
#> SACRAMENTO 0 71 0 0
#> WALNUT_GROVE 0 0 0 0
#> WEST_SACRAMENTO 0 0 0 0
#> WILTON 0 0 0 0
#> <NA> 0 0 0 0
tidy(rec, number = 1)
#> # A tibble: 3 × 3
#> terms retained id
#> <chr> <chr> <chr>
#> 1 city ELK_GROVE other_HsPSC
#> 2 city SACRAMENTO other_HsPSC
#> 3 zip z95823 other_HsPSC
# novel levels are also "othered"
tahiti <- Sacramento[1, ]
tahiti$zip <- "a magical place"
bake(rec, tahiti)
#> Warning: There was 1 column that was a factor when the recipe was prepped:
#> 'zip'.
#> This may cause errors when processing new data.
#> # A tibble: 1 × 2
#> city zip
#> <fct> <fct>
#> 1 SACRAMENTO other values
# 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)
#> # A tibble: 2 × 3
#> terms retained id
#> <chr> <chr> <chr>
#> 1 city SACRAMENTO other_2VUP1
#> 2 zip z95823 other_2VUP1
# compare it to
# sacr_tr %>% count(city, sort = TRUE) %>% top_n(4)
# sacr_tr %>% count(zip, sort = TRUE) %>% top_n(3)