step_novel creates a specification of a recipe
step that will assign a previously unseen factor level to a
step_novel( recipe, ..., role = NA, trained = FALSE, new_level = "new", objects = NULL, skip = FALSE, id = rand_id("novel") ) # S3 method for step_novel tidy(x, ...)
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose which
variables that will be affected by the step. These variables
should be character or factor types. See
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
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 existing steps (if any). For the
tidy method, a tibble with columns
columns that will be affected) and
value (the factor
levels that is used for the new value)
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.
library(modeldata) data(okc) okc_tr <- okc[1:30000,] okc_te <- okc[30001:30006,] okc_te$diet <- "cannibalism" okc_te$diet <- "vampirism" rec <- recipe(~ diet + location, data = okc_tr) rec <- rec %>% step_novel(diet, location) rec <- prep(rec, training = okc_tr) processed <- bake(rec, okc_te) tibble(old = okc_te$diet, new = processed$diet)#> # A tibble: 6 x 2 #> old new #> <chr> <fct> #> 1 vegetarian vegetarian #> 2 strictly anything strictly anything #> 3 cannibalism new #> 4 vampirism new #> 5 NA NA #> 6 NA NAtidy(rec, number = 1)#> # A tibble: 2 x 3 #> terms value id #> <chr> <chr> <chr> #> 1 diet new novel_3AY0w #> 2 location new novel_3AY0w