...

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

R/ensemble_fselect.R

@@ -20,18 +20,15 @@
 #' 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].
 #'
 #' @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