% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ncl.R
\name{step_ncl}
\alias{step_ncl}
\alias{tidy.step_ncl}
\title{Neighborhood Cleaning Rule}
\usage{
step_ncl(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  neighbors = 3,
  distance = "euclidean",
  threshold_clean = 0.5,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  distance_with = recipes::all_predictors(),
  id = rand_id("ncl")
)
}
\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{threshold_clean}{A numeric. Majority classes are only cleaned around
minority class observations when their size is greater than
\code{threshold_clean} times the size of the minority class. Defaults to \code{0.5}.}

\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_ncl()} creates a \emph{specification} of a recipe step that removes majority
class observations that are noisy or that pollute the neighborhood of
minority class observations.
}
\details{
The Neighborhood Cleaning Rule (NCL) is a cleaning method that combines two
passes over the data. First, it applies the Edited Nearest Neighbors rule,
removing majority class observations whose class differs from the majority of
their \code{neighbors} nearest neighbors. Second, for each minority class
observation that is itself misclassified by its neighbors, the majority class
observations among those neighbors are removed. Compared to Edited Nearest
Neighbors, this focuses the cleaning on the neighborhoods of minority class
observations.

The smallest class is treated as the minority class. Only majority classes
larger than \code{threshold_clean} times the size of the minority class are
cleaned in the second pass.

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{threshold_clean}: Threshold (type: double, default: 0.5)
}
}

\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_ncl(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 NCL") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))

recipe(class ~ x + y, data = circle_example) |>
  step_ncl(class) |>
  prep() |>
  bake(new_data = NULL) |>
  ggplot(aes(x, y, color = class)) +
  geom_point() +
  labs(title = "With NCL") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))
\dontshow{\}) # examplesIf}
}
\references{
Laurikkala, J. (2001). Improving identification of difficult
small classes by balancing class distribution. In Conference on Artificial
Intelligence in Medicine in Europe (pp. 63-66). Springer.
}
\seealso{
\code{\link[=ncl]{ncl()}} for direct implementation

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