step_nzv() creates a specification of a recipe step that will potentially
remove variables that are highly sparse and unbalanced.
step_nzv(
recipe,
...,
role = NA,
trained = FALSE,
freq_cut = 95/5,
unique_cut = 10,
options = list(freq_cut = 95/5, unique_cut = 10),
removals = NULL,
skip = FALSE,
id = rand_id("nzv")
)An updated version of recipe with the new step added to the
sequence of any existing operations.
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 selections() for more details.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
Numeric parameters for the filtering process. See the Details section below.
A list of options for the filter (see Details below).
A character string that contains the names of
columns that should be removed. These values are not determined
until prep() is called.
A logical. Should the step be skipped when the
recipe is baked by bake()? While all operations are baked
when prep() 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.
When you tidy() this step, a tibble is returned with
columns terms and id:
character, the selectors or variables selected
character, id of this step
This step has 2 tuning parameters:
freq_cut: Frequency Distribution Ratio (type: double, default: 95/5)
unique_cut: % Unique Values (type: double, default: 10)
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.
This step can potentially remove columns from the data set. This may cause issues for subsequent steps in your recipe if the missing columns are specifically referenced by name. To avoid this, see the advice in the Tips for saving recipes and filtering columns section of selections.
This step diagnoses predictors that have one unique value (i.e. are zero variance predictors) or predictors that have both of the following characteristics:
they have very few unique values relative to the number of samples and
the ratio of the frequency of the most common value to the frequency of the second most common value is large.
For example, an example of near-zero variance predictor is one that, for 1000 samples, has two distinct values and 999 of them are a single value.
To be flagged, first, the frequency of the most prevalent value
over the second most frequent value (called the "frequency
ratio") must be above freq_cut. Secondly, the "percent of
unique values," the number of unique values divided by the total
number of samples (times 100), must also be below
unique_cut.
In the above example, the frequency ratio is 999 and the unique value percent is 0.2%.
Other variable filter steps:
step_corr(),
step_filter_missing(),
step_lincomb(),
step_rm(),
step_select(),
step_zv()
data(biomass, package = "modeldata")
biomass$sparse <- c(1, rep(0, nrow(biomass) - 1))
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
rec <- recipe(HHV ~ carbon + hydrogen + oxygen +
nitrogen + sulfur + sparse,
data = biomass_tr
)
nzv_filter <- rec %>%
step_nzv(all_predictors())
filter_obj <- prep(nzv_filter, training = biomass_tr)
filtered_te <- bake(filter_obj, biomass_te)
any(names(filtered_te) == "sparse")
tidy(nzv_filter, number = 1)
tidy(filter_obj, number = 1)
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