step_nzv {recipes} | R Documentation |
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, options = list(freq_cut = 95/5, unique_cut = 10), removals = NULL)
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 evaluated by the filtering bake. 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. |
options |
A list of options for the filter (see Details below). |
removals |
A character string that contains the names of columns that
should be removed. These values are not determined until
|
This step diagnoses predictors that have one unique value (i.e. are zero variance predictors) or predictors that are 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 percentage is 0.0001.
An updated version of recipe
with the
new step added to the sequence of existing steps (if any).
step_corr
recipe
prep.recipe
bake.recipe
data(biomass) 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")