% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PipeOpNearmiss.R
\name{mlr_pipeops_nearmiss}
\alias{mlr_pipeops_nearmiss}
\alias{PipeOpNearmiss}
\title{Nearmiss Down-Sampling}
\format{
\code{\link[R6:R6Class]{R6Class}} object inheriting from \code{\link{PipeOpTaskPreproc}}/\code{\link{PipeOp}}.
}
\description{
Generates a more balanced data set by down-sampling the instances of non-minority classes using the NEARMISS algorithm.

The algorithm down-samples by selecting instances from the non-minority classes that have the smallest mean distance
to their \code{k} nearest neighbors of different classes.
For this only numeric and integer features are taken into account. These must have no missing values.

This can only be applied to \link[mlr3:TaskClassif]{classification tasks}. Multiclass classification is supported.

See \code{\link[themis:nearmiss]{themis::nearmiss}} for details.
}
\section{Construction}{


\if{html}{\out{<div class="sourceCode">}}\preformatted{PipeOpNearmiss$new(id = "nearmiss", param_vals = list())
}\if{html}{\out{</div>}}
\itemize{
\item \code{id} :: \code{character(1)}\cr
Identifier of resulting object, default \code{"nearmiss"}.
\item \code{param_vals} :: named \code{list}\cr
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default \code{list()}.
}
}

\section{Input and Output Channels}{

Input and output channels are inherited from \code{\link{PipeOpTaskPreproc}}.

The output during training is the input \code{\link[mlr3:Task]{Task}} with the rows removed from the non-minority classes.
The output during prediction is the unchanged input.
}

\section{State}{

The \verb{$state} is a named \code{list} with the \verb{$state} elements inherited from \code{\link{PipeOpTaskPreproc}}.
}

\section{Parameters}{

The parameters are the parameters inherited from \code{\link{PipeOpTaskPreproc}}, as well as
\itemize{
\item \code{k} :: \code{integer(1)}\cr
Number of nearest neighbors used for calculating the mean distances. Default is \code{5}.
\item \code{under_ratio} :: \code{numeric(1)}\cr
Ratio of the minority-to-majority frequencies. This specifies the ratio to which the number of instances
in the non-minority classes get down-sampled to, relative to the number of instances of the minority class.
Default is \code{1}. For details, see \code{\link[themis:nearmiss]{themis::nearmiss}}.
}
}

\section{Fields}{

Only fields inherited from \code{\link{PipeOpTaskPreproc}}/\code{\link{PipeOp}}.
}

\section{Methods}{

Only methods inherited from \code{\link{PipeOpTaskPreproc}}/\code{\link{PipeOp}}.
}

\examples{
\dontshow{if (requireNamespace("themis")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
library("mlr3")

# Create example task
task = tsk("wine")
task$head()
table(task$data(cols = "type"))

# Down-sample and balance data
pop = po("nearmiss")
nearmiss_result = pop$train(list(task))[[1]]$data()
nrow(nearmiss_result)
table(nearmiss_result$type)
\dontshow{\}) # examplesIf}
}
\references{
Zhang, J., Mani, I. (2003).
\dQuote{KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction.}
In \emph{Proceedings of Workshop on Learning from Imbalanced Datasets (ICML)}.
}
\seealso{
https://mlr-org.com/pipeops.html

Other PipeOps: 
\code{\link{PipeOp}},
\code{\link{PipeOpEnsemble}},
\code{\link{PipeOpImpute}},
\code{\link{PipeOpTargetTrafo}},
\code{\link{PipeOpTaskPreproc}},
\code{\link{PipeOpTaskPreprocSimple}},
\code{\link{mlr_pipeops}},
\code{\link{mlr_pipeops_adas}},
\code{\link{mlr_pipeops_blsmote}},
\code{\link{mlr_pipeops_boxcox}},
\code{\link{mlr_pipeops_branch}},
\code{\link{mlr_pipeops_chunk}},
\code{\link{mlr_pipeops_classbalancing}},
\code{\link{mlr_pipeops_classifavg}},
\code{\link{mlr_pipeops_classweights}},
\code{\link{mlr_pipeops_colapply}},
\code{\link{mlr_pipeops_collapsefactors}},
\code{\link{mlr_pipeops_colroles}},
\code{\link{mlr_pipeops_copy}},
\code{\link{mlr_pipeops_datefeatures}},
\code{\link{mlr_pipeops_encode}},
\code{\link{mlr_pipeops_encodeimpact}},
\code{\link{mlr_pipeops_encodelmer}},
\code{\link{mlr_pipeops_featureunion}},
\code{\link{mlr_pipeops_filter}},
\code{\link{mlr_pipeops_fixfactors}},
\code{\link{mlr_pipeops_histbin}},
\code{\link{mlr_pipeops_ica}},
\code{\link{mlr_pipeops_imputeconstant}},
\code{\link{mlr_pipeops_imputehist}},
\code{\link{mlr_pipeops_imputelearner}},
\code{\link{mlr_pipeops_imputemean}},
\code{\link{mlr_pipeops_imputemedian}},
\code{\link{mlr_pipeops_imputemode}},
\code{\link{mlr_pipeops_imputeoor}},
\code{\link{mlr_pipeops_imputesample}},
\code{\link{mlr_pipeops_kernelpca}},
\code{\link{mlr_pipeops_learner}},
\code{\link{mlr_pipeops_missind}},
\code{\link{mlr_pipeops_modelmatrix}},
\code{\link{mlr_pipeops_multiplicityexply}},
\code{\link{mlr_pipeops_multiplicityimply}},
\code{\link{mlr_pipeops_mutate}},
\code{\link{mlr_pipeops_nmf}},
\code{\link{mlr_pipeops_nop}},
\code{\link{mlr_pipeops_ovrsplit}},
\code{\link{mlr_pipeops_ovrunite}},
\code{\link{mlr_pipeops_pca}},
\code{\link{mlr_pipeops_proxy}},
\code{\link{mlr_pipeops_quantilebin}},
\code{\link{mlr_pipeops_randomprojection}},
\code{\link{mlr_pipeops_randomresponse}},
\code{\link{mlr_pipeops_regravg}},
\code{\link{mlr_pipeops_removeconstants}},
\code{\link{mlr_pipeops_renamecolumns}},
\code{\link{mlr_pipeops_replicate}},
\code{\link{mlr_pipeops_rowapply}},
\code{\link{mlr_pipeops_scale}},
\code{\link{mlr_pipeops_scalemaxabs}},
\code{\link{mlr_pipeops_scalerange}},
\code{\link{mlr_pipeops_select}},
\code{\link{mlr_pipeops_smote}},
\code{\link{mlr_pipeops_smotenc}},
\code{\link{mlr_pipeops_spatialsign}},
\code{\link{mlr_pipeops_subsample}},
\code{\link{mlr_pipeops_targetinvert}},
\code{\link{mlr_pipeops_targetmutate}},
\code{\link{mlr_pipeops_targettrafoscalerange}},
\code{\link{mlr_pipeops_textvectorizer}},
\code{\link{mlr_pipeops_threshold}},
\code{\link{mlr_pipeops_tomek}},
\code{\link{mlr_pipeops_tunethreshold}},
\code{\link{mlr_pipeops_unbranch}},
\code{\link{mlr_pipeops_updatetarget}},
\code{\link{mlr_pipeops_vtreat}},
\code{\link{mlr_pipeops_yeojohnson}}
}
\concept{PipeOps}
