% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/enn.R
\name{step_enn}
\alias{step_enn}
\alias{tidy.step_enn}
\title{Edited Nearest Neighbors}
\usage{
step_enn(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  neighbors = 3,
  distance = "euclidean",
  times = 1,
  all_k = FALSE,
  kind_sel = "mode",
  skip = TRUE,
  seed = sample.int(10^5, 1),
  distance_with = recipes::all_predictors(),
  id = rand_id("enn")
)
}
\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{neighbors}{An integer. Number of nearest neighbor that are used to
decide whether an observation is removed. Defaults to \code{3}, unlike the
over-sampling steps which default to \code{5}.}

\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{times}{A positive integer for the maximum number of times ENN is
applied. Defaults to \code{1} for a single pass. Values greater than \code{1} repeat
the cleaning, stopping early once a pass removes no observations. Use
\code{Inf} to repeat until convergence (Repeated Edited Nearest Neighbors).}

\item{all_k}{A logical. When \code{TRUE}, ENN is applied with an increasing number
of neighbors, from \code{1} up to \code{neighbors}, cleaning the data at each step
(All k-Nearest Neighbors). Takes precedence over \code{times}. Defaults to
\code{FALSE}.}

\item{kind_sel}{A character string. The rule used to decide whether an
observation is removed. \code{"mode"} (the default) removes an observation when
the majority of its neighbors disagree with its class. \code{"all"} is stricter
and removes an observation unless all of its neighbors share its class.}

\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_enn()} creates a \emph{specification} of a recipe step that removes
observations whose class differs from the majority of their nearest
neighbors.
}
\details{
Edited Nearest Neighbors (ENN) is a cleaning method. For each observation it
finds the \code{neighbors} nearest neighbors and, if the class of the observation
does not match the majority class among those neighbors, the observation is
removed. This tends to remove noisy and borderline observations, which can
lead to smoother decision boundaries.

Setting \code{times} greater than 1 applies ENN repeatedly, removing more noisy
and borderline observations on each pass and stopping early once a pass
removes nothing. This corresponds to Repeated Edited Nearest Neighbors
(RENN).

Setting \code{all_k = TRUE} applies ENN with increasing numbers of neighbors, from
\code{1} up to \code{neighbors}, cleaning the data at each step. This corresponds to
All k-Nearest Neighbors (AllKNN) and takes precedence over \code{times}.

Setting \code{kind_sel = "all"} uses a stricter cleaning rule: instead of removing
an observation when the majority of its neighbors disagree, it is removed
unless every one of its neighbors shares its class. This removes more
observations than the default \code{kind_sel = "mode"}.

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{Minimum observations}{
The data must have at least \code{neighbors + 1} observations for the nearest
neighbors to be computed.
}

\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: 3)
\item \code{all_k}: Pruning (type: logical, default: FALSE)
}
}

\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_enn(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 ENN") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))

recipe(class ~ x + y, data = circle_example) |>
  step_enn(class) |>
  prep() |>
  bake(new_data = NULL) |>
  ggplot(aes(x, y, color = class)) +
  geom_point() +
  labs(title = "With ENN") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))
\dontshow{\}) # examplesIf}
}
\references{
Wilson, D. L. (1972). Asymptotic properties of nearest neighbor
rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics,
(3), 408-421.

Tomek, I. (1976). An experiment with the edited nearest-neighbor rule. IEEE
Transactions on Systems, Man, and Cybernetics, (6), 448-452.
}
\seealso{
\code{\link[=enn]{enn()}} for direct implementation

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

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_instance_hardness]{step_instance_hardness()}},
\code{\link[=step_ncl]{step_ncl()}},
\code{\link[=step_nearmiss]{step_nearmiss()}},
\code{\link[=step_oss]{step_oss()}},
\code{\link[=step_tomek]{step_tomek()}}
}
\concept{Steps for under-sampling}
