% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/smogn.R
\name{step_smogn}
\alias{step_smogn}
\alias{tidy.step_smogn}
\title{Apply SMOGN Algorithm}
\usage{
step_smogn(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  threshold = 0.5,
  relevance = NULL,
  neighbors = 5,
  perturbation = 0.02,
  distance = "euclidean",
  indicator_column = NULL,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  id = rand_id("smogn")
)
}
\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
variable is used to sample the data. See \link[recipes:selections]{recipes::selections}
for more details. The selection should result in \emph{single
numeric variable}. For the \code{tidy} method, these are not
currently used.}

\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{column}{A character string of the variable name that will
be populated (eventually) by the \code{...} selectors.}

\item{threshold}{A number between 0 and 1. Outcome values with a relevance at
or above this value are treated as rare and over-sampled. Defaults to \code{0.5}.}

\item{relevance}{A matrix of relevance control points, or \code{NULL} (default).
When \code{NULL}, relevance is derived automatically from the boxplot extremes of
the outcome. When supplied, the first column gives outcome values and the
second column their relevance in \verb{[0, 1]}.}

\item{neighbors}{An integer. Number of nearest neighbor that are used
to generate the new examples of the rare values.}

\item{perturbation}{A number. The magnitude of the Gaussian noise added when
generating synthetic examples in unsafe regions. Defaults to \code{0.02}.}

\item{distance}{A character string specifying the distance metric used for
nearest neighbor calculations, defaulting to \code{"euclidean"}. The available
metrics fall into three groups.

\code{"euclidean"}, \code{"cosine"}, and \code{"mahalanobis"} use approximate nearest
neighbors via the RANN package and scale well to large datasets.

\code{"squared_chord"}, \code{"matusita"}, \code{"hellinger"}, and \code{"bhattacharyya"} are
probability-divergence measures that treat each row as a distribution over
the predictors, so they require non-negative values. \code{"hellinger"} and
\code{"bhattacharyya"} further require each row to sum to 1. All four also use
the RANN package and scale well to large datasets.

\code{"manhattan"}, \code{"chebyshev"}, \code{"canberra"}, \code{"soergel"}, \code{"lorentzian"},
\code{"jeffreys"}, \code{"topsoe"}, \code{"jensen-shannon"}, \code{"jensen_difference"},
\code{"taneja"}, and \code{"kumar-johnson"} compute an exact all-pairs distance
matrix. This takes time and memory proportional to the square of the number
of observations in a class, so these are best suited to smaller datasets.
Everything from \code{"canberra"} onwards is a probability divergence requiring
non-negative values, is provided by the philentropy package (which must be
installed separately), and in the case of \code{"jeffreys"}, \code{"taneja"}, and
\code{"kumar-johnson"} requires strictly positive values, since those divide by
individual predictor values.

The probability divergences are meaningful for compositional predictors such
as proportions or counts normalized per observation, and are generally not
appropriate for standardized predictors.}

\item{indicator_column}{A single string or \code{NULL} (the default). If a
string is given, a logical column with that name is added to the output,
marking rows added by the step (\code{TRUE}) vs rows from the original data
(\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{seed}{An integer that will be used as the seed when applied.}

\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 existing steps (if any). For the
\code{tidy} method, a tibble with columns \code{terms} which is
the variable used to sample.
}
\description{
\code{step_smogn()} creates a \emph{specification} of a recipe step that generates new
examples for imbalanced regression problems using SMOGN.
}
\details{
SMOGN is a pre-processing approach for imbalanced regression. A relevance
function assigns each outcome value a relevance score, and values with a
relevance at or above \code{threshold} are treated as rare. The data is split
into contiguous bins of rare and common outcome values. Common bins are
under-sampled and rare bins are over-sampled toward a balanced size. New
rare examples are generated either by interpolating between an example and a
nearby neighbor (when they are close enough to be considered safe) or by
perturbing the example with Gaussian noise (when they are not), where the
amount of noise is controlled by \code{perturbation}.

By default relevance is derived automatically from the boxplot extremes of
the outcome, giving the median a relevance of 0 and the extreme values a
relevance of 1. A matrix of relevance control points can instead be supplied
through \code{relevance}, with the first column giving outcome values and the
second column their relevance.

All columns in the data are sampled and returned by \code{\link[recipes:juice]{recipes::juice()}}
and \code{\link[recipes:bake]{recipes::bake()}}.

All columns used in this step must be numeric with no missing data.

When used in modeling, users should strongly consider using the
option \code{skip = TRUE} so that the extra sampling is \emph{not}
conducted outside of the training set.
}
\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{Tuning Parameters}{
This step has 2 tuning parameters:
\itemize{
\item \code{neighbors}: # Nearest Neighbors (type: integer, default: 5)
\item \code{threshold}: Threshold (type: double, default: 0.5)
}
}

\section{Case weights}{


The underlying operation does not allow for case weights. Supplying data
with a case weights column to this step results in an error.
}

\examples{
\dontshow{if (rlang::is_installed("ggplot2")) withAutoprint(\{ # examplesIf}
library(recipes)
library(ggplot2)

ggplot(circle_example, aes(x)) +
  geom_histogram(bins = 30) +
  labs(title = "Without SMOGN")

recipe(y ~ x, data = circle_example) |>
  step_smogn(y) |>
  prep() |>
  bake(new_data = NULL) |>
  ggplot(aes(y)) +
  geom_histogram(bins = 30) +
  labs(title = "With SMOGN")
\dontshow{\}) # examplesIf}
}
\references{
Branco, P., Torgo, L., and Ribeiro, R. P. (2017). SMOGN: a
pre-processing approach for imbalanced regression. Proceedings of Machine
Learning Research, 74:36-50.
}
\seealso{
\code{\link[=smogn]{smogn()}} for direct implementation

Other Steps for over-sampling:
\code{\link[=step_adasyn]{step_adasyn()}},
\code{\link[=step_bsmote]{step_bsmote()}},
\code{\link[=step_kmeans_smote]{step_kmeans_smote()}},
\code{\link[=step_rose]{step_rose()}},
\code{\link[=step_smote]{step_smote()}},
\code{\link[=step_smoten]{step_smoten()}},
\code{\link[=step_smotenc]{step_smotenc()}},
\code{\link[=step_svmsmote]{step_svmsmote()}},
\code{\link[=step_upsample]{step_upsample()}}
}
\concept{Steps for over-sampling}
