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mlr_learners_classif.lda.Rd
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mlr_learners_classif.lda.Rd
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LearnerClassifLDA.R
\name{mlr_learners_classif.lda}
\alias{mlr_learners_classif.lda}
\alias{LearnerClassifLDA}
\title{Linear Discriminant Analysis Classification Learner}
\description{
Linear discriminant analysis.
Calls \code{\link[MASS:lda]{MASS::lda()}} from package \CRANpkg{MASS}.
}
\details{
Parameters \code{method} and \code{prior} exist for training and prediction but
accept different values for each. Therefore, arguments for
the predict stage have been renamed to \code{predict.method} and \code{predict.prior},
respectively.
}
\section{Dictionary}{
This \link{Learner} can be instantiated via the \link[mlr3misc:Dictionary]{dictionary} \link{mlr_learners} or with the associated sugar function \code{\link[=lrn]{lrn()}}:
\if{html}{\out{<div class="sourceCode">}}\preformatted{mlr_learners$get("classif.lda")
lrn("classif.lda")
}\if{html}{\out{</div>}}
}
\section{Meta Information}{
\itemize{
\item Task type: \dQuote{classif}
\item Predict Types: \dQuote{response}, \dQuote{prob}
\item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{factor}, \dQuote{ordered}
\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{MASS}
}
}
\section{Parameters}{
\tabular{lllll}{
Id \tab Type \tab Default \tab Levels \tab Range \cr
dimen \tab untyped \tab - \tab \tab - \cr
method \tab character \tab moment \tab moment, mle, mve, t \tab - \cr
nu \tab integer \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr
predict.method \tab character \tab plug-in \tab plug-in, predictive, debiased \tab - \cr
predict.prior \tab untyped \tab - \tab \tab - \cr
prior \tab untyped \tab - \tab \tab - \cr
tol \tab numeric \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr
}
}
\examples{
if (requireNamespace("MASS", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.lda")
print(learner)
# Define a Task
task = tsk("sonar")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
# print the model
print(learner$model)
# importance method
if("importance" \%in\% learner$properties) print(learner$importance)
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
}
}
\references{
Venables WN, Ripley BD (2002).
\emph{Modern Applied Statistics with S}, Fourth edition.
Springer, New York.
ISBN 0-387-95457-0, \url{http://www.stats.ox.ac.uk/pub/MASS4/}.
}
\seealso{
\itemize{
\item Chapter in the \href{https://mlr3book.mlr-org.com/}{mlr3book}:
\url{https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners}
\item Package \href{https://github.com/mlr-org/mlr3extralearners}{mlr3extralearners} for more learners.
\item \link[mlr3misc:Dictionary]{Dictionary} of \link[=Learner]{Learners}: \link{mlr_learners}
\item \code{as.data.table(mlr_learners)} for a table of available \link[=Learner]{Learners} in the running session (depending on the loaded packages).
\item \CRANpkg{mlr3pipelines} to combine learners with pre- and postprocessing steps.
\item Extension packages for additional task types:
\itemize{
\item \CRANpkg{mlr3proba} for probabilistic supervised regression and survival analysis.
\item \CRANpkg{mlr3cluster} for unsupervised clustering.
}
\item \CRANpkg{mlr3tuning} for tuning of hyperparameters, \CRANpkg{mlr3tuningspaces}
for established default tuning spaces.
}
Other Learner:
\code{\link{mlr_learners_classif.cv_glmnet}},
\code{\link{mlr_learners_classif.glmnet}},
\code{\link{mlr_learners_classif.kknn}},
\code{\link{mlr_learners_classif.log_reg}},
\code{\link{mlr_learners_classif.multinom}},
\code{\link{mlr_learners_classif.naive_bayes}},
\code{\link{mlr_learners_classif.nnet}},
\code{\link{mlr_learners_classif.qda}},
\code{\link{mlr_learners_classif.ranger}},
\code{\link{mlr_learners_classif.svm}},
\code{\link{mlr_learners_classif.xgboost}},
\code{\link{mlr_learners_regr.cv_glmnet}},
\code{\link{mlr_learners_regr.glmnet}},
\code{\link{mlr_learners_regr.kknn}},
\code{\link{mlr_learners_regr.km}},
\code{\link{mlr_learners_regr.lm}},
\code{\link{mlr_learners_regr.nnet}},
\code{\link{mlr_learners_regr.ranger}},
\code{\link{mlr_learners_regr.svm}},
\code{\link{mlr_learners_regr.xgboost}}
}
\concept{Learner}
\section{Super classes}{
\code{\link[mlr3:Learner]{mlr3::Learner}} -> \code{\link[mlr3:LearnerClassif]{mlr3::LearnerClassif}} -> \code{LearnerClassifLDA}
}
\section{Methods}{
\subsection{Public methods}{
\itemize{
\item \href{#method-LearnerClassifLDA-new}{\code{LearnerClassifLDA$new()}}
\item \href{#method-LearnerClassifLDA-clone}{\code{LearnerClassifLDA$clone()}}
}
}
\if{html}{\out{
<details><summary>Inherited methods</summary>
<ul>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="base_learner"><a href='../../mlr3/html/Learner.html#method-Learner-base_learner'><code>mlr3::Learner$base_learner()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="format"><a href='../../mlr3/html/Learner.html#method-Learner-format'><code>mlr3::Learner$format()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="help"><a href='../../mlr3/html/Learner.html#method-Learner-help'><code>mlr3::Learner$help()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="predict"><a href='../../mlr3/html/Learner.html#method-Learner-predict'><code>mlr3::Learner$predict()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="predict_newdata"><a href='../../mlr3/html/Learner.html#method-Learner-predict_newdata'><code>mlr3::Learner$predict_newdata()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="print"><a href='../../mlr3/html/Learner.html#method-Learner-print'><code>mlr3::Learner$print()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="reset"><a href='../../mlr3/html/Learner.html#method-Learner-reset'><code>mlr3::Learner$reset()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="train"><a href='../../mlr3/html/Learner.html#method-Learner-train'><code>mlr3::Learner$train()</code></a></span></li>
</ul>
</details>
}}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-LearnerClassifLDA-new"></a>}}
\if{latex}{\out{\hypertarget{method-LearnerClassifLDA-new}{}}}
\subsection{Method \code{new()}}{
Creates a new instance of this \link[R6:R6Class]{R6} class.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LearnerClassifLDA$new()}\if{html}{\out{</div>}}
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-LearnerClassifLDA-clone"></a>}}
\if{latex}{\out{\hypertarget{method-LearnerClassifLDA-clone}{}}}
\subsection{Method \code{clone()}}{
The objects of this class are cloneable with this method.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LearnerClassifLDA$clone(deep = FALSE)}\if{html}{\out{</div>}}
}
\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{deep}}{Whether to make a deep clone.}
}
\if{html}{\out{</div>}}
}
}
}