% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/smogn_impl.R
\name{smogn}
\alias{smogn}
\title{SMOGN Algorithm}
\usage{
smogn(
  df,
  var,
  threshold = 0.5,
  relevance = NULL,
  neighbors = 5,
  perturbation = 0.02,
  distance = "euclidean"
)
}
\arguments{
\item{df}{data.frame or tibble. Must have 1 numeric outcome variable and
remaining numeric variables.}

\item{var}{Character, name of the numeric outcome variable.}

\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.}
}
\value{
A data.frame or tibble, depending on type of \code{df}.
}
\description{
SMOGN generates new examples for imbalanced regression problems. It
identifies rare regions of the continuous outcome using a relevance
function, over-samples those regions with a combination of SMOTE-style
interpolation and the introduction of Gaussian noise, and under-samples the
common regions.
}
\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 used in this function must be numeric with no missing data.
}
\examples{
circle_numeric <- circle_example[, c("x", "y")]

res <- smogn(circle_numeric, var = "x")

res <- smogn(circle_numeric, var = "x", neighbors = 10)
}
\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[=step_smogn]{step_smogn()}} for step function of this method

Other Direct Implementations:
\code{\link[=adasyn]{adasyn()}},
\code{\link[=bsmote]{bsmote()}},
\code{\link[=cluster_centroids]{cluster_centroids()}},
\code{\link[=cnn]{cnn()}},
\code{\link[=enn]{enn()}},
\code{\link[=instance_hardness]{instance_hardness()}},
\code{\link[=kmeans_smote]{kmeans_smote()}},
\code{\link[=ncl]{ncl()}},
\code{\link[=nearmiss]{nearmiss()}},
\code{\link[=oss]{oss()}},
\code{\link[=rose]{rose()}},
\code{\link[=smote]{smote()}},
\code{\link[=smoten]{smoten()}},
\code{\link[=smotenc]{smotenc()}},
\code{\link[=svmsmote]{svmsmote()}},
\code{\link[=tomek]{tomek()}}
}
\concept{Direct Implementations}
