% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nzv.R
\name{step_nzv}
\alias{step_nzv}
\title{Near-Zero Variance Filter}
\usage{
step_nzv(recipe, ..., role = NA, trained = FALSE, options = list(freq_cut
  = 95/5, unique_cut = 10), removals = NULL)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the sequence of 
operations for this recipe.}

\item{...}{One or more selector functions to choose which variables that
will evaluated by the filtering bake. See \code{\link{selections}} for
more details.}

\item{role}{Not used by this step since no new variables are created.}

\item{trained}{A logical to indicate if the quantities for preprocessing 
have been estimated.}

\item{options}{A list of options for the filter (see Details below).}

\item{removals}{A character string that contains the names of columns that
should be removed. These values are not determined until
\code{\link{prep.recipe}} is called.}
}
\value{
An updated version of \code{recipe} with the
  new step added to the sequence of existing steps (if any).
}
\description{
\code{step_nzv} creates a \emph{specification} of a recipe step that will
  potentially remove variables that are highly sparse and unbalanced.
}
\details{
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:
\enumerate{
  \item they have very few unique values relative to the number of samples
    and
  \item 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
  \code{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 \code{unique_cut}.

In the above example, the frequency ratio is 999 and the unique value
  percentage is 0.0001.
}
\examples{
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")
}
\seealso{
\code{\link{step_corr}} \code{\link{recipe}}
  \code{\link{prep.recipe}} \code{\link{bake.recipe}}
}
\concept{
preprocessing variable_filters
}
\keyword{datagen}
