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, default to
"other".
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.
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.
When you tidy() this step, a tibble is returned with
columns terms, retained , and id:
character, the selectors or variables selected
character, factor levels not pulled into "other"
character, id of this step
This step has 1 tuning parameters:
threshold: Threshold (type: double, default: 0.05)
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.
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()
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`
#> ℹ 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)