Simple value assignments for novel factor levels

step_novel() creates a specification of a recipe step that will assign a previously unseen factor level to "new".

Usage

step_novel(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  new_level = "new",
  objects = NULL,
  skip = FALSE,
  id = rand_id("novel")
)

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.

new_level

A single character value that will be assigned to new factor levels.

objects

A list of objects that contain the information on factor levels that will be determined by prep().

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.

Value

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

Details

The selected variables are adjusted to have a new level (given by new_level) that is placed in the last position. During preparation there will be no data points associated with this new level since all of the data have been seen.

Note that if the original columns are character, they will be converted to factors by this step.

Missing values will remain missing.

If new_level is already in the data given to prep, an error is thrown.

When fitting a model that can deal with new factor levels, consider using workflows::add_recipe() with allow_novel_levels = TRUE set in hardhat::default_recipe_blueprint(). This will allow your model to handle new levels at prediction time, instead of throwing warnings or errors.

Tidying

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

terms

character, the selectors or variables selected

value

character, the factor levels that are used for the new value

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

See also

dummy_names()

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_num2factor(), step_ordinalscore(), step_other(), step_regex(), step_relevel(), step_string2factor(), step_time(), step_unknown(), step_unorder()

Examples

data(Sacramento, package = "modeldata")

sacr_tr <- Sacramento[1:800, ]
sacr_te <- Sacramento[801:806, ]

# Without converting the predictor to a character, the new level would be converted
# to `NA`.
sacr_te$city <- as.character(sacr_te$city)
sacr_te$city[3] <- "beeptown"
sacr_te$city[4] <- "boopville"
sacr_te$city <- as.factor(sacr_te$city)

rec <- recipe(~ city + zip, data = sacr_tr)

rec <- rec %>%
  step_novel(city, zip)
rec <- prep(rec, training = sacr_tr)

processed <- bake(rec, sacr_te)
tibble(old = sacr_te$city, new = processed$city)
#> # A tibble: 6 × 2
#>   old        new       
#>   <fct>      <fct>     
#> 1 SACRAMENTO SACRAMENTO
#> 2 AUBURN     AUBURN    
#> 3 beeptown   new       
#> 4 boopville  new       
#> 5 SACRAMENTO SACRAMENTO
#> 6 ROSEVILLE  ROSEVILLE 

tidy(rec, number = 1)
#> # A tibble: 2 × 3
#>   terms value id         
#>   <chr> <chr> <chr>      
#> 1 city  new   novel_3AY0w
#> 2 zip   new   novel_3AY0w