% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tomek.R
\name{step_tomek}
\alias{step_tomek}
\alias{tidy.step_tomek}
\title{Remove Tomek's Links}
\usage{
step_tomek(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  distance = "euclidean",
  skip = TRUE,
  seed = sample.int(10^5, 1),
  distance_with = recipes::all_predictors(),
  id = rand_id("tomek")
)
}
\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
factor 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{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{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{distance_with}{A call to a selector function to choose
which variables are used for distance calculations. Defaults to
\code{\link[recipes:all_predictors]{recipes::all_predictors()}}. The variable selected by \code{...} is
always excluded from the distance calculations.}

\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_tomek()} creates a \emph{specification} of a recipe step that removes
majority class instances of tomek links.
}
\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 variables selected by \code{distance_with} must be numeric with no missing
data.

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

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{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("modeldata")) withAutoprint(\{ # examplesIf}
library(recipes)
library(modeldata)
data(hpc_data)

hpc_data0 <- hpc_data |>
  select(-protocol, -day)

orig <- count(hpc_data0, class, name = "orig")
orig

up_rec <- recipe(class ~ ., data = hpc_data0) |>
  step_tomek(class) |>
  prep()

training <- up_rec |>
  bake(new_data = NULL) |>
  count(class, name = "training")
training

# Since `skip` defaults to TRUE, baking the step has no effect
baked <- up_rec |>
  bake(new_data = hpc_data0) |>
  count(class, name = "baked")
baked

orig |>
  left_join(training, by = "class") |>
  left_join(baked, by = "class")

library(ggplot2)

ggplot(circle_example, aes(x, y, color = class)) +
  geom_point() +
  labs(title = "Without Tomek") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))

recipe(class ~ x + y, data = circle_example) |>
  step_tomek(class) |>
  prep() |>
  bake(new_data = NULL) |>
  ggplot(aes(x, y, color = class)) +
  geom_point() +
  labs(title = "With Tomek") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))
\dontshow{\}) # examplesIf}
}
\references{
Tomek. Two modifications of cnn. IEEE Trans. Syst. Man Cybern.,
6:769-772, 1976.
}
\seealso{
\code{\link[=tomek]{tomek()}} for direct implementation

\code{\link[=step_smote]{step_smote()}}, which is commonly composed before \code{step_tomek()} to clean
the ambiguous points that over-sampling creates near the class boundary
(the equivalent of imbalanced-learn's \code{SMOTETomek}).

Other Steps for under-sampling:
\code{\link[=step_cluster_centroids]{step_cluster_centroids()}},
\code{\link[=step_cnn]{step_cnn()}},
\code{\link[=step_downsample]{step_downsample()}},
\code{\link[=step_enn]{step_enn()}},
\code{\link[=step_instance_hardness]{step_instance_hardness()}},
\code{\link[=step_ncl]{step_ncl()}},
\code{\link[=step_nearmiss]{step_nearmiss()}},
\code{\link[=step_oss]{step_oss()}}
}
\concept{Steps for under-sampling}
