From 1a046eed358bd915a4982c34b0c374ee6d2cce6c Mon Sep 17 00:00:00 2001
From: be-marc <marcbecker@posteo.de>
Date: Thu, 16 Jan 2025 09:55:47 +0100
Subject: [PATCH] ...

---
 R/EnsembleFSResult.R      | 2 +-
 R/ensemble_fselect.R      | 7 ++-----
 man/ensemble_fs_result.Rd | 2 +-
 man/ensemble_fselect.Rd   | 9 ++-------
 4 files changed, 6 insertions(+), 14 deletions(-)

diff --git a/R/EnsembleFSResult.R b/R/EnsembleFSResult.R
index 73560eee..3ab903a9 100644
--- a/R/EnsembleFSResult.R
+++ b/R/EnsembleFSResult.R
@@ -83,7 +83,7 @@ EnsembleFSResult = R6Class("EnsembleFSResult",
     #'  The column with the performance scores on the inner resampling of the train sets is not mandatory,
     #'  but note that it should be named as `{inner_measure$id}_inner` to distinguish from
     #'  the `{measure$id}`.
-    #' @param features ([character()])\cr
+    #' @param features (`character()`)\cr
     #'  The vector of features of the task that was used in the ensemble feature
     #'  selection.
     #' @param benchmark_result ([mlr3::BenchmarkResult])\cr
diff --git a/R/ensemble_fselect.R b/R/ensemble_fselect.R
index d59d6049..81bef951 100644
--- a/R/ensemble_fselect.R
+++ b/R/ensemble_fselect.R
@@ -20,8 +20,7 @@
 #' The result object also includes the performance scores calculated during the inner resampling of the training sets, using models with the best feature subsets.
 #' These scores are stored in a column named `{measure_id}_inner`.
 #'
-#' @section Note:
-#'
+#' @note
 #' The **active measure** of performance is the one applied to the test sets.
 #' This is preferred, as inner resampling scores on the training sets are likely to be overestimated when using the final models.
 #' Users can change the active measure by using the `set_active_measure()` method of the [EnsembleFSResult].
@@ -29,9 +28,7 @@
 #' @param learners (list of [mlr3::Learner])\cr
 #'  The learners to be used for feature selection.
 #' @param init_resampling ([mlr3::Resampling])\cr
-#'  The initial resampling strategy of the data, from which each train set
-#'  will be passed on to the [auto_fselector] to optimize the learners and
-#'  perform feature selection.
+#'  The initial resampling strategy of the data, from which each train set will be passed on to the [auto_fselector] to optimize the learners and perform feature selection.
 #'  Each test set will be used for prediction on the final models returned by [auto_fselector].
 #'  Can only be [mlr3::ResamplingSubsampling] or [mlr3::ResamplingBootstrap].
 #' @param inner_resampling ([mlr3::Resampling])\cr
diff --git a/man/ensemble_fs_result.Rd b/man/ensemble_fs_result.Rd
index 434a0530..77fa2314 100644
--- a/man/ensemble_fs_result.Rd
+++ b/man/ensemble_fs_result.Rd
@@ -158,7 +158,7 @@ The column with the performance scores on the inner resampling of the train sets
 but note that it should be named as \verb{\{inner_measure$id\}_inner} to distinguish from
 the \code{{measure$id}}.}
 
-\item{\code{features}}{(\code{\link[=character]{character()}})\cr
+\item{\code{features}}{(\code{character()})\cr
 The vector of features of the task that was used in the ensemble feature
 selection.}
 
diff --git a/man/ensemble_fselect.Rd b/man/ensemble_fselect.Rd
index baa5ead5..da46a7f4 100644
--- a/man/ensemble_fselect.Rd
+++ b/man/ensemble_fselect.Rd
@@ -45,9 +45,7 @@ Task to operate on.}
 The learners to be used for feature selection.}
 
 \item{init_resampling}{(\link[mlr3:Resampling]{mlr3::Resampling})\cr
-The initial resampling strategy of the data, from which each train set
-will be passed on to the \link{auto_fselector} to optimize the learners and
-perform feature selection.
+The initial resampling strategy of the data, from which each train set will be passed on to the \link{auto_fselector} to optimize the learners and perform feature selection.
 Each test set will be used for prediction on the final models returned by \link{auto_fselector}.
 Can only be \link[mlr3:mlr_resamplings_subsampling]{mlr3::ResamplingSubsampling} or \link[mlr3:mlr_resamplings_bootstrap]{mlr3::ResamplingBootstrap}.}
 
@@ -94,14 +92,11 @@ Results are stored in an \link{EnsembleFSResult}.
 The result object also includes the performance scores calculated during the inner resampling of the training sets, using models with the best feature subsets.
 These scores are stored in a column named \verb{\{measure_id\}_inner}.
 }
-\section{Note}{
-
-
+\note{
 The \strong{active measure} of performance is the one applied to the test sets.
 This is preferred, as inner resampling scores on the training sets are likely to be overestimated when using the final models.
 Users can change the active measure by using the \code{set_active_measure()} method of the \link{EnsembleFSResult}.
 }
-
 \examples{
 \donttest{
   efsr = ensemble_fselect(