diff --git a/man-roxygen/example.R b/man-roxygen/example.R
index da144ce0..571eba99 100644
--- a/man-roxygen/example.R
+++ b/man-roxygen/example.R
@@ -2,11 +2,32 @@
 lrn = mlr3::lrn(id)
 pkgs = setdiff(lrn$packages, c("mlr3", "mlr3learners"))
 %>
+#' <% task_id = if ("LearnerClassif" %in% class(lrn(id))) "sonar" else "mtcars" %>
+#'
 #' @examples
 #' if (<%= paste0("requireNamespace(\"", pkgs, "\", quietly = TRUE)", collapse = " && ") %>) {
-#'   learner = mlr3::lrn("<%= id %>")
-#'   print(learner)
+#' # Define the Learner and set parameter values
+#' <%= sprintf("learner = lrn(\"%s\")", id)%>
+#' print(learner)
+#'
+#' # Define a Task
+#' <%= sprintf("task = tsk(\"%s\")", task_id)%>
+#'
+#' # Create train and test set
+#' <%= sprintf("ids = partition(task)")%>
+#'
+#' # Train the learner on the training ids
+#' <%= sprintf("learner$train(task, row_ids = ids$train)")%>
+#'
+#' # print the model
+#' print(learner$model)
+#'
+#' # importance method
+#' if("importance" %in% learner$properties) print(learner$importance)
+#'
+#' # Make predictions for the test rows
+#' <%= sprintf("predictions = learner$predict(task, row_ids = ids$test)")%>
 #'
-#'   # available parameters:
-#' learner$param_set$ids()
+#' # Score the predictions
+#' predictions$score()
 #' }
diff --git a/man/mlr_learners_classif.cv_glmnet.Rd b/man/mlr_learners_classif.cv_glmnet.Rd
index c40f3a1d..140479e7 100644
--- a/man/mlr_learners_classif.cv_glmnet.Rd
+++ b/man/mlr_learners_classif.cv_glmnet.Rd
@@ -87,11 +87,30 @@ as the first factor level.
 
 \examples{
 if (requireNamespace("glmnet", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.cv_glmnet")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.cv_glmnet")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_classif.glmnet.Rd b/man/mlr_learners_classif.glmnet.Rd
index 3d63350f..657684fe 100644
--- a/man/mlr_learners_classif.glmnet.Rd
+++ b/man/mlr_learners_classif.glmnet.Rd
@@ -96,11 +96,30 @@ as the first factor level.
 
 \examples{
 if (requireNamespace("glmnet", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.glmnet")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.glmnet")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_classif.kknn.Rd b/man/mlr_learners_classif.kknn.Rd
index 32a0f04d..879ad900 100644
--- a/man/mlr_learners_classif.kknn.Rd
+++ b/man/mlr_learners_classif.kknn.Rd
@@ -63,11 +63,30 @@ lrn("classif.kknn")
 
 \examples{
 if (requireNamespace("kknn", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.kknn")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.kknn")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_classif.lda.Rd b/man/mlr_learners_classif.lda.Rd
index b5a78306..8f3f65fa 100644
--- a/man/mlr_learners_classif.lda.Rd
+++ b/man/mlr_learners_classif.lda.Rd
@@ -48,11 +48,30 @@ lrn("classif.lda")
 
 \examples{
 if (requireNamespace("MASS", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.lda")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.lda")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_classif.log_reg.Rd b/man/mlr_learners_classif.log_reg.Rd
index 7a2593f8..e4af38fe 100644
--- a/man/mlr_learners_classif.log_reg.Rd
+++ b/man/mlr_learners_classif.log_reg.Rd
@@ -84,11 +84,30 @@ Instead, set the respective hyperparameter or use \CRANpkg{mlr3pipelines} to cre
 
 \examples{
 if (requireNamespace("stats", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.log_reg")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.log_reg")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \seealso{
diff --git a/man/mlr_learners_classif.multinom.Rd b/man/mlr_learners_classif.multinom.Rd
index 33628134..c4fe069f 100644
--- a/man/mlr_learners_classif.multinom.Rd
+++ b/man/mlr_learners_classif.multinom.Rd
@@ -53,11 +53,30 @@ lrn("classif.multinom")
 
 \examples{
 if (requireNamespace("nnet", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.multinom")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.multinom")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \seealso{
diff --git a/man/mlr_learners_classif.naive_bayes.Rd b/man/mlr_learners_classif.naive_bayes.Rd
index bc131580..74035c12 100644
--- a/man/mlr_learners_classif.naive_bayes.Rd
+++ b/man/mlr_learners_classif.naive_bayes.Rd
@@ -38,11 +38,30 @@ lrn("classif.naive_bayes")
 
 \examples{
 if (requireNamespace("e1071", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.naive_bayes")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.naive_bayes")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \seealso{
diff --git a/man/mlr_learners_classif.nnet.Rd b/man/mlr_learners_classif.nnet.Rd
index 85f8e694..e35c915e 100644
--- a/man/mlr_learners_classif.nnet.Rd
+++ b/man/mlr_learners_classif.nnet.Rd
@@ -73,11 +73,30 @@ lrn("classif.nnet")
 
 \examples{
 if (requireNamespace("nnet", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.nnet")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.nnet")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_classif.qda.Rd b/man/mlr_learners_classif.qda.Rd
index 5542e2d9..70181c7d 100644
--- a/man/mlr_learners_classif.qda.Rd
+++ b/man/mlr_learners_classif.qda.Rd
@@ -46,11 +46,30 @@ lrn("classif.qda")
 
 \examples{
 if (requireNamespace("MASS", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.qda")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.qda")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_classif.ranger.Rd b/man/mlr_learners_classif.ranger.Rd
index 7edcb87a..ff7ab887 100644
--- a/man/mlr_learners_classif.ranger.Rd
+++ b/man/mlr_learners_classif.ranger.Rd
@@ -61,6 +61,7 @@ lrn("classif.ranger")
    importance \tab character \tab - \tab none, impurity, impurity_corrected, permutation \tab - \cr
    keep.inbag \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    max.depth \tab integer \tab NULL \tab  \tab \eqn{[0, \infty)}{[0, Inf)} \cr
+   min.bucket \tab integer \tab 1 \tab  \tab \eqn{[1, \infty)}{[1, Inf)} \cr
    min.node.size \tab integer \tab NULL \tab  \tab \eqn{[1, \infty)}{[1, Inf)} \cr
    min.prop \tab numeric \tab 0.1 \tab  \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr
    minprop \tab numeric \tab 0.1 \tab  \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr
@@ -88,11 +89,30 @@ lrn("classif.ranger")
 
 \examples{
 if (requireNamespace("ranger", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.ranger")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.ranger")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_classif.svm.Rd b/man/mlr_learners_classif.svm.Rd
index 393e29c9..2328fe53 100644
--- a/man/mlr_learners_classif.svm.Rd
+++ b/man/mlr_learners_classif.svm.Rd
@@ -51,11 +51,30 @@ lrn("classif.svm")
 
 \examples{
 if (requireNamespace("e1071", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.svm")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.svm")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_classif.xgboost.Rd b/man/mlr_learners_classif.xgboost.Rd
index 5c366ebb..a5e55e48 100644
--- a/man/mlr_learners_classif.xgboost.Rd
+++ b/man/mlr_learners_classif.xgboost.Rd
@@ -88,6 +88,7 @@ lrn("classif.xgboost")
    colsample_bylevel \tab numeric \tab 1 \tab  \tab \eqn{[0, 1]}{[0, 1]} \cr
    colsample_bynode \tab numeric \tab 1 \tab  \tab \eqn{[0, 1]}{[0, 1]} \cr
    colsample_bytree \tab numeric \tab 1 \tab  \tab \eqn{[0, 1]}{[0, 1]} \cr
+   device \tab untyped \tab cpu \tab  \tab - \cr
    disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    early_stopping_rounds \tab integer \tab NULL \tab  \tab \eqn{[1, \infty)}{[1, Inf)} \cr
    early_stopping_set \tab character \tab none \tab none, train, test \tab - \cr
@@ -118,7 +119,6 @@ lrn("classif.xgboost")
    one_drop \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    outputmargin \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    predcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
-   predictor \tab character \tab cpu_predictor \tab cpu_predictor, gpu_predictor \tab - \cr
    predinteraction \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    predleaf \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    print_every_n \tab integer \tab 1 \tab  \tab \eqn{[1, \infty)}{[1, Inf)} \cr
@@ -148,11 +148,30 @@ lrn("classif.xgboost")
 
 \examples{
 if (requireNamespace("xgboost", quietly = TRUE)) {
-  learner = mlr3::lrn("classif.xgboost")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("classif.xgboost")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("sonar")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 
 # Train learner with early stopping on spam data set
diff --git a/man/mlr_learners_regr.cv_glmnet.Rd b/man/mlr_learners_regr.cv_glmnet.Rd
index 4b9c6202..d7c6d1c1 100644
--- a/man/mlr_learners_regr.cv_glmnet.Rd
+++ b/man/mlr_learners_regr.cv_glmnet.Rd
@@ -80,11 +80,30 @@ lrn("regr.cv_glmnet")
 
 \examples{
 if (requireNamespace("glmnet", quietly = TRUE)) {
-  learner = mlr3::lrn("regr.cv_glmnet")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("regr.cv_glmnet")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("mtcars")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_regr.glmnet.Rd b/man/mlr_learners_regr.glmnet.Rd
index 0a1a08e0..9a263a83 100644
--- a/man/mlr_learners_regr.glmnet.Rd
+++ b/man/mlr_learners_regr.glmnet.Rd
@@ -96,11 +96,30 @@ lrn("regr.glmnet")
 
 \examples{
 if (requireNamespace("glmnet", quietly = TRUE)) {
-  learner = mlr3::lrn("regr.glmnet")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("regr.glmnet")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("mtcars")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_regr.kknn.Rd b/man/mlr_learners_regr.kknn.Rd
index 4fb55533..77610851 100644
--- a/man/mlr_learners_regr.kknn.Rd
+++ b/man/mlr_learners_regr.kknn.Rd
@@ -63,11 +63,30 @@ lrn("regr.kknn")
 
 \examples{
 if (requireNamespace("kknn", quietly = TRUE)) {
-  learner = mlr3::lrn("regr.kknn")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("regr.kknn")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("mtcars")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_regr.km.Rd b/man/mlr_learners_regr.km.Rd
index ebc1b878..8bbd7fe6 100644
--- a/man/mlr_learners_regr.km.Rd
+++ b/man/mlr_learners_regr.km.Rd
@@ -72,11 +72,30 @@ lrn("regr.km")
 
 \examples{
 if (requireNamespace("DiceKriging", quietly = TRUE)) {
-  learner = mlr3::lrn("regr.km")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("regr.km")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("mtcars")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_regr.lm.Rd b/man/mlr_learners_regr.lm.Rd
index 03ae4565..e043af12 100644
--- a/man/mlr_learners_regr.lm.Rd
+++ b/man/mlr_learners_regr.lm.Rd
@@ -41,6 +41,9 @@ lrn("regr.lm")
    singular.ok \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr
    x \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    y \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
+   rankdeficient \tab character \tab - \tab warnif, simple, non-estim, NA, NAwarn \tab - \cr
+   tol \tab numeric \tab 1e-07 \tab  \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr
+   verbose \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
 }
 }
 
@@ -58,11 +61,30 @@ Instead, set the respective hyperparameter or use \CRANpkg{mlr3pipelines} to cre
 
 \examples{
 if (requireNamespace("stats", quietly = TRUE)) {
-  learner = mlr3::lrn("regr.lm")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("regr.lm")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("mtcars")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \seealso{
diff --git a/man/mlr_learners_regr.nnet.Rd b/man/mlr_learners_regr.nnet.Rd
index 50bfb588..6dbecf0a 100644
--- a/man/mlr_learners_regr.nnet.Rd
+++ b/man/mlr_learners_regr.nnet.Rd
@@ -73,11 +73,30 @@ lrn("regr.nnet")
 
 \examples{
 if (requireNamespace("nnet", quietly = TRUE)) {
-  learner = mlr3::lrn("regr.nnet")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("regr.nnet")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("mtcars")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_regr.ranger.Rd b/man/mlr_learners_regr.ranger.Rd
index cb848580..b297c93d 100644
--- a/man/mlr_learners_regr.ranger.Rd
+++ b/man/mlr_learners_regr.ranger.Rd
@@ -36,6 +36,7 @@ lrn("regr.ranger")
    importance \tab character \tab - \tab none, impurity, impurity_corrected, permutation \tab - \cr
    keep.inbag \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    max.depth \tab integer \tab NULL \tab  \tab \eqn{[0, \infty)}{[0, Inf)} \cr
+   min.bucket \tab integer \tab 1 \tab  \tab \eqn{[1, \infty)}{[1, Inf)} \cr
    min.node.size \tab integer \tab 5 \tab  \tab \eqn{[1, \infty)}{[1, Inf)} \cr
    min.prop \tab numeric \tab 0.1 \tab  \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr
    minprop \tab numeric \tab 0.1 \tab  \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr
@@ -88,11 +89,30 @@ Note that \code{mtry} and \code{mtry.ratio} are mutually exclusive.
 
 \examples{
 if (requireNamespace("ranger", quietly = TRUE)) {
-  learner = mlr3::lrn("regr.ranger")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("regr.ranger")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("mtcars")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_regr.svm.Rd b/man/mlr_learners_regr.svm.Rd
index 96b39afc..8a4eb6ee 100644
--- a/man/mlr_learners_regr.svm.Rd
+++ b/man/mlr_learners_regr.svm.Rd
@@ -49,11 +49,30 @@ lrn("regr.svm")
 
 \examples{
 if (requireNamespace("e1071", quietly = TRUE)) {
-  learner = mlr3::lrn("regr.svm")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("regr.svm")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("mtcars")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 }
 \references{
diff --git a/man/mlr_learners_regr.xgboost.Rd b/man/mlr_learners_regr.xgboost.Rd
index d4ea721b..656db9e6 100644
--- a/man/mlr_learners_regr.xgboost.Rd
+++ b/man/mlr_learners_regr.xgboost.Rd
@@ -49,6 +49,7 @@ lrn("regr.xgboost")
    colsample_bylevel \tab numeric \tab 1 \tab  \tab \eqn{[0, 1]}{[0, 1]} \cr
    colsample_bynode \tab numeric \tab 1 \tab  \tab \eqn{[0, 1]}{[0, 1]} \cr
    colsample_bytree \tab numeric \tab 1 \tab  \tab \eqn{[0, 1]}{[0, 1]} \cr
+   device \tab untyped \tab cpu \tab  \tab - \cr
    disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    early_stopping_rounds \tab integer \tab NULL \tab  \tab \eqn{[1, \infty)}{[1, Inf)} \cr
    early_stopping_set \tab character \tab none \tab none, train, test \tab - \cr
@@ -79,7 +80,6 @@ lrn("regr.xgboost")
    one_drop \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    outputmargin \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    predcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
-   predictor \tab character \tab cpu_predictor \tab cpu_predictor, gpu_predictor \tab - \cr
    predinteraction \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    predleaf \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
    print_every_n \tab integer \tab 1 \tab  \tab \eqn{[1, \infty)}{[1, Inf)} \cr
@@ -147,11 +147,30 @@ would error. Just setting a nonsense default to workaround this.
 
 \examples{
 if (requireNamespace("xgboost", quietly = TRUE)) {
-  learner = mlr3::lrn("regr.xgboost")
-  print(learner)
+# Define the Learner and set parameter values
+learner = lrn("regr.xgboost")
+print(learner)
 
-  # available parameters:
-learner$param_set$ids()
+# Define a Task
+task = tsk("mtcars")
+
+# Create train and test set
+ids = partition(task)
+
+# Train the learner on the training ids
+learner$train(task, row_ids = ids$train)
+
+# print the model
+print(learner$model)
+
+# importance method
+if("importance" \%in\% learner$properties) print(learner$importance)
+
+# Make predictions for the test rows
+predictions = learner$predict(task, row_ids = ids$test)
+
+# Score the predictions
+predictions$score()
 }
 
 # Train learner with early stopping on spam data set