step_novel() creates a specification of a recipe step that will assign a
previously unseen factor level to "new".
step_novel(
recipe,
...,
role = NA,
trained = FALSE,
new_level = "new",
objects = NULL,
skip = FALSE,
id = rand_id("novel")
)An updated version of recipe with the new step added to the
sequence of any existing operations.
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 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.
When you tidy() this step, a tibble is returned with
columns terms, value , and id:
character, the selectors or variables selected
character, the factor levels that are used for the new value
character, id of this step
The underlying operation does not allow for case weights.
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.
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()
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)
tidy(rec, number = 1)
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