% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PipeOpThreshold.R
\name{mlr_pipeops_threshold}
\alias{mlr_pipeops_threshold}
\alias{PipeOpThreshold}
\title{Change the Threshold of a Classification Prediction}
\format{
\code{\link[R6:R6Class]{R6Class}} inheriting from \code{\link{PipeOp}}.
}
\description{
Change the threshold of a \code{Prediction} during the \code{predict} step.
The incoming \code{\link[mlr3:Learner]{Learner}}'s \verb{$predict_type} needs to be \code{"prob"}.
Internally calls \code{PredictionClassif$set_threshold}.
}
\section{Construction}{


\if{html}{\out{<div class="sourceCode">}}\preformatted{PipeOpThreshold$new(id = "threshold", param_vals = list())
}\if{html}{\out{</div>}}
\itemize{
\item \code{id} :: \code{character(1)}
Identifier of the resulting  object, default \code{"threshold"}.
\item \code{param_vals} :: named \code{list}\cr
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction.
Defaults to \code{numeric(0)}.
}
}

\section{Input and Output Channels}{

During training, the input and output are \code{NULL}.
A \code{\link[mlr3:PredictionClassif]{PredictionClassif}} is required as input and returned as output during prediction.
}

\section{State}{

The \verb{$state} is left empty (\code{list()}).
}

\section{Parameters}{

\itemize{
\item \code{thresholds} :: \code{numeric}\cr
A numeric vector of thresholds for the different class levels.
May have length 1 for binary classification predictions, must
otherwise have length of the number of target classes; see
\code{\link[mlr3:PredictionClassif]{PredictionClassif}}'s \verb{$set_threshold()} method.
Initialized to \code{0.5}, i.e. thresholding for binary classification
at level \code{0.5}.
}
}

\section{Fields}{

Fields inherited from \code{\link{PipeOp}}, as well as:
\itemize{
\item \code{predict_type} :: \code{character(1)}\cr
Type of prediction to return. Either \code{"prob"} (default) or \code{"response"}.
Setting to \code{"response"} should rarely be used; it may potentially save some memory but has
no other benefits.
}
}

\section{Methods}{

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

\examples{
\dontshow{if (requireNamespace("rpart")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
library("mlr3")
t = tsk("german_credit")
gr = po(lrn("classif.rpart", predict_type = "prob")) \%>>\%
  po("threshold", param_vals = list(thresholds = 0.9))
gr$train(t)
gr$predict(t)
\dontshow{\}) # examplesIf}
}
\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_nearmiss}},
\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_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}
