step_percentile {recipes} | R Documentation |
Percentile transformation
Description
step_percentile()
creates a specification of a recipe step that replaces
the value of a variable with its percentile from the training set.
Usage
step_percentile(
recipe,
...,
role = NA,
trained = FALSE,
ref_dist = NULL,
options = list(probs = (0:100)/100),
outside = "none",
skip = FALSE,
id = rand_id("percentile")
)
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 |
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
ref_dist |
The computed percentiles is stored here once this
preprocessing step has be trained by |
options |
A named list of options to pass to |
outside |
A character, describing how interpolation is to take place
outside the interval |
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. |
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
, percentile
, and id
:
- terms
character, the selectors or variables selected
- value
numeric, the value at the percentile
- percentile
numeric, the percentile as a percentage
- id
character, id of this step
Case weights
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in case_weights and the examples on
tidymodels.org
.
See Also
Other individual transformation steps:
step_BoxCox()
,
step_YeoJohnson()
,
step_bs()
,
step_harmonic()
,
step_hyperbolic()
,
step_inverse()
,
step_invlogit()
,
step_log()
,
step_logit()
,
step_mutate()
,
step_ns()
,
step_poly()
,
step_relu()
,
step_sqrt()
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_percentile(carbon)
prepped_rec <- prep(rec)
prepped_rec %>%
bake(biomass_te)
tidy(rec, 1)
tidy(prepped_rec, 1)