step_discretize {recipes} | R Documentation |
Discretize Numeric Variables
Description
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).
Usage
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")
)
Arguments
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. |
Details
Note that missing values will be turned into a factor level with the level
prefix_missing
, where prefix
is specified in the options
argument.
Value
An updated version of recipe
with the new step added to the
sequence of any existing operations.
Tidying
When you tidy()
this step, a tibble is returned with
columns terms
, value
, and id
:
- terms
character, the selectors or variables selected
- value
numeric, the breaks
- id
character, id of this step
Tuning Parameters
This step has 2 tuning parameters:
-
min_unique
: Unique Value Threshold (type: integer, default: 10) -
num_breaks
: Number of Cut Points (type: integer, default: 4)
Case weights
The underlying operation does not allow for case weights.
See Also
Other discretization steps:
step_cut()
Examples
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)