Zero Variance Filter
step_zv creates a specification of a recipe step
that will remove variables that contain only a single value.
step_zv(
recipe,
...,
role = NA,
trained = FALSE,
removals = NULL,
skip = FALSE,
id = rand_id("zv")
)
## S3 method for class 'step_zv'
tidy(x, ...)recipe |
A recipe object. The step will be added to the sequence of operations for this recipe. |
... |
One or more selector functions to choose which
variables that will be evaluated by the filtering. 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. |
removals |
A character string that contains the names of
columns that should be removed. These values are not determined
until |
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. |
x |
A |
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 which
is the columns that will be removed.
library(modeldata)
data(biomass)
biomass$one_value <- 1
biomass_tr <- biomass[biomass$dataset == "Training",]
biomass_te <- biomass[biomass$dataset == "Testing",]
rec <- recipe(HHV ~ carbon + hydrogen + oxygen +
nitrogen + sulfur + one_value,
data = biomass_tr)
zv_filter <- rec %>%
step_zv(all_predictors())
filter_obj <- prep(zv_filter, training = biomass_tr)
filtered_te <- bake(filter_obj, biomass_te)
any(names(filtered_te) == "one_value")
tidy(zv_filter, number = 1)
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