% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RLearner_regr_randomForest.R
\name{regr.randomForest}
\alias{regr.randomForest}
\title{regression using randomForest.}
\description{
a mlr learner for regrssion tasks using \code{\link[randomForest]{randomForest}}.
}
\details{
if \code{predict.type = "se"} the \code{se.method} (by default \dQuote{jackknife})
is estimated, using the methods described in Sexton and Laake (2009).

If \code{se.method = "bootstrap"} the standard error of a prediction is estimated by bootstrapping the random forest, where the number of bootstrap replicates and the number of trees in the ensemble are controlled by \code{se.boot} and \code{ntree.for.se} respectively, and then taking the standard deviation of the predictions.

If \code{se.method = "jackknife"}, the default, the standard error of a prediction is estimated by computing the jackknife-after-bootstrap, the mean-squared difference between the prediction made by only using trees which did not contain said observation and the ensemble prediction.

For both \dQuote{jackknife} and \dQuote{bootstrap}, a Monte-Carlo bias correction is applied and, in the case that this results in a negative variance estimate, the values are truncated at 0.
}
\references{
[Joseph Sexton] and [Petter Laake],; [Standard errors for bagged and random forest estimators], Computational Statistics and Data Analysis Volume 53, 2009, [801-811].
}
