step_discretize creates a a specification of a recipe
step that will convert numeric data into a factor with
bins having approximately the same number of data points (based
on a training set).
step_discretize(recipe, ..., role = NA, trained = FALSE,
num_breaks = 4, min_unique = 10, objects = NULL,
options = list(), skip = FALSE, id = rand_id("discretize"))# S3 method for step_discretize
tidy(x, ...)
A recipe object. The step will be added to the sequence of operations for this recipe.
For step_discretize, the dots specify
one or more selector functions to choose which variables are
affected by the step. See selections() for more
details. For the tidy method, these are not currently
used.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
An integer defining how many cuts to make of the data.
An integer defining a sample size line of
dignity for the binning. If (the number of unique
values)/(cuts+1) is less than min_unique, no
discretization takes place.
The discretize() objects are stored
here once the recipe has be trained by
prep.recipe().
A list of options to discretize(). A
defaults is set for the argument x. Note that the using
the options prefix and labels when more than one
variable is being transformed might be problematic as all
variables inherit those values.
A logical. Should the step be skipped when the
recipe is baked by bake.recipe()? While all operations are baked
when prep.recipe() 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.
A step_discretize object
step_discretize returns 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 terms (the selectors or variables selected)
and value (the breaks).