step_feature_hash.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/feature_hash.R
\name{step_feature_hash}
\alias{step_feature_hash}
\alias{tidy.step_feature_hash}
\title{Dummy Variables Creation via Feature Hashing}
\usage{
step_feature_hash(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  num_hash = 2^6,
  preserve = deprecated(),
  columns = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("feature_hash")
)
}
\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 variables
for this step. See \code{\link[recipes:selections]{selections()}} for more details.}

\item{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 \emph{predictors} in a model.}

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

\item{num_hash}{The number of resulting dummy variable columns.}

\item{preserve}{Use \code{keep_original_cols} instead to specify whether the
selected column(s) should be retained in addition to the new dummy
variables.}

\item{columns}{A character vector for the selected columns. This is \code{NULL}
until the step is trained by \code{\link[recipes:prep]{recipes::prep()}}.}

\item{keep_original_cols}{A logical to keep the original variables in the
output. Defaults to \code{FALSE}.}

\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[recipes:bake]{bake()}}? While all operations are baked
when \code{\link[recipes:prep]{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 \code{skip = TRUE} as it may affect
the computations for subsequent operations.}

\item{id}{A character string that is unique to this step to identify it.}
}
\value{
An updated version of \code{recipe} with the new step added to the
sequence of any existing operations.
}
\description{
\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#soft-deprecated}{\figure{lifecycle-soft-deprecated.svg}{options: alt='[Soft-deprecated]'}}}{\strong{[Soft-deprecated]}}

\code{step_feature_hash()} is being deprecated in favor of
\code{\link[textrecipes:step_dummy_hash]{textrecipes::step_dummy_hash()}}. This function creates a \emph{specification}
of a recipe step that will convert nominal data (e.g. character or factors)
into one or more numeric binary columns using the levels of the original
data.
}
\details{
\code{step_feature_hash()} will create a set of binary dummy variables from a
factor or character variable. The values themselves are used to determine
which row that the dummy variable should be assigned (as opposed to having a
specific column that the value will map to).

Since this method does not rely on a pre-determined assignment of levels to
columns, new factor levels can be added to the selected columns without
issue. Missing values result in missing values for all of the hashed columns.

Note that the assignment of the levels to the hashing columns does not try to
maximize the allocation. It is likely that multiple levels of the column will
map to the same hashed columns (even with small data sets). Similarly, it is
likely that some columns will have all zeros. A zero-variance filter (via
\code{\link[recipes:step_zv]{recipes::step_zv()}}) is recommended for any recipe that uses hashed columns.
}
\section{Tidying}{
When you \code{\link[recipes:tidy.recipe]{tidy()}} this step, a tibble is returned with
columns \code{terms} and \code{id}:

\describe{
\item{terms}{character, the selectors or variables selected}
\item{id}{character, id of this step}
}
}

\section{Case weights}{


The underlying operation does not allow for case weights.
}

\examples{
\dontshow{if (!embed:::is_cran_check() && rlang::is_installed(c("modeldata", "keras"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
data(grants, package = "modeldata")
rec <-
  recipe(class ~ sponsor_code, data = grants_other) \%>\%
  step_feature_hash(
    sponsor_code,
    num_hash = 2^6, keep_original_cols = TRUE
  ) \%>\%
  prep()

# How many of the 298 locations ended up in each hash column?
results <-
  bake(rec, new_data = NULL, starts_with("sponsor_code")) \%>\%
  distinct()

apply(results \%>\% select(-sponsor_code), 2, sum) \%>\% table()
\dontshow{\}) # examplesIf}
}
\references{
Weinberger, K, A Dasgupta, J Langford, A Smola, and J Attenberg. 2009.
"Feature Hashing for Large Scale Multitask Learning." In Proceedings of the
26th Annual International Conference on Machine Learning, 1113–20. ACM.

Kuhn and Johnson (2020) \emph{Feature Engineering and Selection: A Practical
Approach for Predictive Models}. CRC/Chapman Hall
\url{https://bookdown.org/max/FES/encoding-predictors-with-many-categories.html}
}
\seealso{
\code{\link[recipes:step_dummy]{recipes::step_dummy()}}, \code{\link[recipes:step_zv]{recipes::step_zv()}}
}