diff --git a/man/bestSpecificLearner.Rd b/man/bestSpecificLearner.Rd
new file mode 100644
index 0000000..3e4d453
--- /dev/null
+++ b/man/bestSpecificLearner.Rd
@@ -0,0 +1,32 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/BestSpecificLearner.R
+\name{bestSpecificLearner}
+\alias{bestSpecificLearner}
+\title{The best layer-specific model is used as meta model.}
+\usage{
+bestSpecificLearner(x, y, perf = NULL)
+}
+\arguments{
+\item{x}{\code{data.frame(1)} \cr
+\code{data.frame} of predictors.}
+
+\item{y}{\code{vector(1)} \cr
+Target observations. Either binary or two level factor variable.}
+
+\item{perf}{\verb{function(1)} \cr
+Function to compute layer-specific performance of learners. If NULL, the Brier Score is used by default.
+Otherwise, the performance function must accept two parameters: \code{observed} (observed values) and \code{predicted} (predicted values).}
+}
+\value{
+A model object of class \code{weightedMeanLeaner}.
+}
+\description{
+The meta learner is the best layer-specific laerner.
+}
+\examples{
+set.seed(20240624L)
+x = data.frame(x1 = runif(n = 50L, min = 0, max = 1))
+y = sample(x = 0L:1L, size = 50L, replace = TRUE)
+my_best_model = bestSpecificLearner(x = x, y = y)
+
+}
diff --git a/man/predict.bestSpecificLearner.Rd b/man/predict.bestSpecificLearner.Rd
new file mode 100644
index 0000000..3d1d7d4
--- /dev/null
+++ b/man/predict.bestSpecificLearner.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.bestSpecificLearner.R
+\name{predict.bestSpecificLearner}
+\alias{predict.bestSpecificLearner}
+\title{Weighted mean prediction.}
+\usage{
+\method{predict}{bestSpecificLearner}(object, data, na.rm = TRUE)
+}
+\arguments{
+\item{object}{\code{weightedMeanLearner(1)} \cr
+An object from class \link{weightedMeanLearner}}
+
+\item{data}{\code{data.frame} \cr
+\code{data.frame} to be predicted.}
+
+\item{na.rm}{\cr
+Removes NAs when TRUE.}
+}
+\value{
+Predicted target values are returned.
+}
+\description{
+Predict function for models from class \code{weightedMeanLearner}.
+}
+\examples{
+set.seed(20240625)
+x = data.frame(x1 = runif(n = 50L, min = 0, max = 1))
+y <- sample(x = 0:1, size = 50L, replace = TRUE)
+my_model <- bestSpecificLearner(x = x, y = y)
+x_new <- data.frame(x1 = rnorm(10L))
+my_predictions <- predict(object = my_model, data = x_new)
+
+}