step_dummy_multi_choice: Handle levels in multiple predictors together

In tidymodels/recipes: Preprocessing and Feature Engineering Steps for Modeling

Handle levels in multiple predictors together

Description

step_dummy_multi_choice() creates a specification of a recipe step that will convert multiple nominal data (e.g. characters or factors) into one or more numeric binary model terms for the levels of the original data.

Usage

step_dummy_multi_choice(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  threshold = 0,
  levels = NULL,
  input = NULL,
  other = "other",
  naming = dummy_names,
  prefix = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("dummy_multi_choice")
)

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. The selected variables must be factors.
role	For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
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 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.
levels	A list that contains the information needed to create dummy variables for each variable contained in terms. This is NULL until the step is trained by prep().
input	A character vector containing the names of the columns used. This is NULL until the step is trained by prep().
other	A single character value for the "other" category.
naming	A function that defines the naming convention for new dummy columns. See Details below.
prefix	A character string for the prefix of the resulting new variables. See notes below.
keep_original_cols	A logical to keep the original variables in the output. Defaults to FALSE.
skip	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.
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.

This step produces a number of columns, based on the number of categories it finds. The naming of the columns is determined by the function based on the naming argument. The default is to return ⁠<prefix>_<category name>⁠. By default prefix is NULL, which means the name of the first column selected will be used in place.

This recipe step allows for flexible naming of the resulting variables. For an unordered factor named x, with levels "a" and "b", the default naming convention would be to create a new variable called x_b. The naming format can be changed using the naming argument; the function dummy_names() is the default.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Tuning Parameters

This step has 1 tuning parameters:

• threshold: Threshold (type: double, default: 0)

Tidying

When you tidy() this step, a tibble is returned with columns terms, columns , and id:

terms

character, the selectors or variables selected

columns

character, names of resulting columns

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

See Also

Other dummy variable and encoding steps: step_bin2factor(), step_count(), step_date(), step_dummy(), step_dummy_extract(), step_factor2string(), step_holiday(), step_indicate_na(), step_integer(), step_novel(), step_num2factor(), step_ordinalscore(), step_other(), step_regex(), step_relevel(), step_string2factor(), step_time(), step_unknown(), step_unorder()

Examples

library(tibble)
languages <- tribble(
  ~lang_1,    ~lang_2,   ~lang_3,
  "English",  "Italian", NA,
  "Spanish",  NA,        "French",
  "Armenian", "English", "French",
  NA,         NA,        NA
)

dummy_multi_choice_rec <- recipe(~., data = languages) %>%
  step_dummy_multi_choice(starts_with("lang")) %>%
  prep()

bake(dummy_multi_choice_rec, new_data = NULL)
tidy(dummy_multi_choice_rec, number = 1)

dummy_multi_choice_rec2 <- recipe(~., data = languages) %>%
  step_dummy_multi_choice(starts_with("lang"),
    prefix = "lang",
    threshold = 0.2
  ) %>%
  prep()

bake(dummy_multi_choice_rec2, new_data = NULL)
tidy(dummy_multi_choice_rec2, number = 1)

tidymodels/recipes documentation built on Nov. 29, 2024, 3:05 p.m.