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\name{addLearner}
\alias{addLearner}
\title{Add Learners to \code{\link{LearnerList}}}
\description{
The function can be used to add new learner to \code{\link{LearnerList}} in the
current \R session.
}
\usage{addLearner(x)}
\arguments{
\item{x}{a list containing all required information to define a new learner
(see Details below).}
}
\details{
The function can be used to add new learners to \code{\link{LearnerList}} in
the current \R session. The function expects a list of four elements
including the name of the learners object class, the name of the package
where the class and the fitting method is implemented, the name of the method
and a prediction function that predicts class probabilities (in the
classification case) or numeric values (in the regression case) and takes the
arguments \code{x} (the fitted model object), \code{newdata} a
\code{data.frame} containing the predictions of the observations in the
evaluation sample and \code{yclass} a character string specifying the type
of the response variable (\code{"numeric"}, \code{"factor"}, etc.). The
elements in the list should be named \code{class}, \code{package},
\code{method} and \code{predfun}.
}
\seealso{\code{\link{LearnerList}}}
\examples{
newlearner <- list(
class = "svm",
package = "e1071",
method = "Support Vector Machine",
predict = function(x, newdata, yclass = NULL) {
if(match(yclass, c("ordered", "factor"))) {
attr(predict(x, newdata = newdata, probability = TRUE), "probabilities")
} else {
predict(x, newdata = newdata)
}
})
addLearner(newlearner)
}
\keyword{stability}
\keyword{similarity}
\keyword{resampling}
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