Simple Value Assignments for Novel Factor LevelsSource:
step_novel() creates a specification of a recipe step that will assign a
previously unseen factor level to
step_novel( recipe, ..., role = NA, trained = FALSE, new_level = "new", objects = NULL, skip = FALSE, id = rand_id("novel") )
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 single character value that will be assigned to new factor levels.
A list of objects that contain the information on factor levels that will be determined by
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 = TRUEas 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 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
Note that if the original columns are character, they will be converted to factors by this step.
Missing values will remain missing.
new_level is already in the data given to
prep, an error
When fitting a model that can deal with new factor levels, consider using
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.
tidy() this step, a tibble with columns
terms (the columns that will be affected) and
value (the factor
levels that is used for the new value) is returned.
Other dummy variable and encoding steps:
data(Sacramento, package = "modeldata") sacr_tr <- Sacramento[1:800, ] sacr_te <- Sacramento[801:806, ] sacr_te$city <- "beeptown" #> Warning: invalid factor level, NA generated sacr_te$city <- "boopville" #> Warning: invalid factor level, NA generated 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 NA NA #> 4 NA NA #> 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