% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tomek_impl.R
\name{tomek}
\alias{tomek}
\title{Remove Tomek's Links}
\usage{
tomek(df, var, 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{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{
Removes the majority class member of each pair of observations that form a
Tomek link.
}
\details{
A Tomek link is a pair of points from different classes that are each
other's nearest neighbors. Such pairs sit on or very near the decision
boundary and are considered noise or borderline cases. The algorithm
identifies all Tomek links and removes the majority class instance from each
pair, cleaning the class boundary without discarding non-boundary majority
examples. Because only boundary points are removed, this typically discards
far fewer observations than other under-sampling methods.

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

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

res <- tomek(circle_numeric, var = "class", distance = "manhattan")
}
\references{
Tomek. Two modifications of cnn. IEEE Trans. Syst. Man Cybern.,
6:769-772, 1976.
}
\seealso{
\code{\link[=step_tomek]{step_tomek()}} 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[=smote]{smote()}},
\code{\link[=smoten]{smoten()}},
\code{\link[=smotenc]{smotenc()}},
\code{\link[=svmsmote]{svmsmote()}}
}
\concept{Direct Implementations}
