step_discretize | R Documentation |
step_discretize
creates 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(prefix = "bin"), skip = FALSE, id = rand_id("discretize") )
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 |
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. |
num_breaks |
An integer defining how many cuts to make of the data. |
min_unique |
An integer defining a sample size line of
dignity for the binning. If (the number of unique
values) |
objects |
The |
options |
A list of options to |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
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.
When you tidy()
this step, a tibble with columns
terms
(the selectors or variables selected) and value
(the breaks) is returned.
The underlying operation does not allow for case weights.
Other discretization steps:
step_cut()
data(biomass, package = "modeldata") biomass_tr <- biomass[biomass$dataset == "Training", ] biomass_te <- biomass[biomass$dataset == "Testing", ] rec <- recipe( HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur, data = biomass_tr ) %>% step_discretize(carbon, hydrogen) rec <- prep(rec, biomass_tr) binned_te <- bake(rec, biomass_te) table(binned_te$carbon) tidy(rec, 1)
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