% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/smote_impl.R
\name{smote}
\alias{smote}
\title{SMOTE Algorithm}
\usage{
smote(df, var, k = 5, over_ratio = 1, distance = "euclidean")
}
\arguments{
\item{df}{data.frame or tibble. Must have 1 factor variable and remaining
numeric variables.}

\item{var}{Character, name of variable containing factor variable.}

\item{k}{An integer. Number of nearest neighbor that are used
to generate the new examples of the minority class.}

\item{over_ratio}{A numeric value for the ratio of the
minority-to-majority frequencies. The default value (1) means
that all other levels are sampled up to have the same
frequency as the most occurring level. A value of 0.5 would mean
that the minority levels will have (at most) (approximately)
half as many rows as the majority level.

A named numeric vector can be used instead to give different levels
different targets, for example \code{c(a = 1, b = 0.5)}. The names must be
levels of the outcome and the values are ratios of the majority level,
exactly as in the single-number case. Levels that are not named are left
untouched, as are rows with a missing outcome. Because a vector of targets
is not a single value, supplying one means this argument can no longer be
tuned. See \code{vignette("ratio", package = "themis")} for more details.}

\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{
SMOTE generates new examples of the minority class using nearest neighbors
of these cases.
}
\details{
SMOTE generates new examples of the minority class using nearest neighbors
of these cases. For each existing minority class example, new examples are
created by interpolating between the example and its nearest neighbors. The
number of nearest neighbors used is controlled by the number of neighbors
argument (\code{k} in \code{\link[=smote]{smote()}}, \code{neighbors} in \code{\link[=step_smote]{step_smote()}}), and the number
of new examples generated is controlled by \code{over_ratio}.

All columns used in this function must be numeric with no missing data.
}
\examples{
circle_numeric <- circle_example[, c("x", "y", "class")]

res <- smote(circle_numeric, var = "class")

res <- smote(circle_numeric, var = "class", k = 10)

res <- smote(circle_numeric, var = "class", over_ratio = 0.8)

res <- smote(circle_numeric, var = "class", distance = "manhattan")
}
\references{
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer,
W. P. (2002). Smote: Synthetic minority over-sampling technique.
Journal of Artificial Intelligence Research, 16:321-357.
}
\seealso{
\code{\link[=step_smote]{step_smote()}} 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[=smogn]{smogn()}},
\code{\link[=smoten]{smoten()}},
\code{\link[=smotenc]{smotenc()}},
\code{\link[=svmsmote]{svmsmote()}},
\code{\link[=tomek]{tomek()}}
}
\concept{Direct Implementations}
