diff --git a/book/chapters/appendices/solutions.qmd b/book/chapters/appendices/solutions.qmd
index 78d056659..b2be7288b 100644
--- a/book/chapters/appendices/solutions.qmd
+++ b/book/chapters/appendices/solutions.qmd
@@ -1342,8 +1342,9 @@ This improves the average error of our model by a further 1600$.
 
 ## Solutions to @sec-technical
 
-1. Consider the following example where you resample a learner (debug learner, sleeps for 3 seconds during train) on 4 workers using the multisession backend:
-```{r technical-050, eval = FALSE}
+1. Consider the following example where you resample a learner (debug learner, sleeps for 3 seconds during `$train()`) on 4 workers using the multisession backend:
+
+```{r technical-050}
 task = tsk("penguins")
 learner = lrn("classif.debug", sleep_train = function() 3)
 resampling = rsmp("cv", folds = 6)
@@ -1356,12 +1357,12 @@ i. Assuming that the learner would actually calculate something and not just sle
 ii. Prove your point by measuring the elapsed time, e.g., using `r ref("system.time()")`.
 iii. What would you change in the setup and why?
 
-Not all CPUs would be utilized in the example.
-All 4 of them are occupied for the first 4 iterations of the cross validation.
+Not all CPUs would be utilized for the whole duration.
+All 4 of them are occupied for the first 4 iterations of the cross-validation.
 The 5th iteration, however, only runs in parallel to the 6th fold, leaving 2 cores idle.
 This is supported by the elapsed time of roughly 6 seconds for 6 jobs compared to also roughly 6 seconds for 8 jobs:
 
-```{r solutions-022, eval = FALSE}
+```{r solutions-022}
 task = tsk("penguins")
 learner = lrn("classif.debug", sleep_train = function() 3)
 
@@ -1377,72 +1378,175 @@ system.time(resample(task, learner, resampling))
 If possible, the number of resampling iterations should be an integer multiple of the number of workers.
 Therefore, a simple adaptation either increases the number of folds for improved accuracy of the error estimate or reduces the number of folds for improved runtime.
 
-2. Create a new custom classification measure (either using methods demonstrated in @sec-extending or with `msr("classif.costs")` which scores predictions using the mean over the following classification costs:
-
-* If the learner predicted label "A" and the truth is "A", assign score 0
-* If the learner predicted label "B" and the truth is "B", assign score 0
-* If the learner predicted label "A" and the truth is "B", assign score 1
-* If the learner predicted label "B" and the truth is "A", assign score 10
+2. Create a new custom binary classification measure which scores ("prob"-type) predictions.
+  This measure should compute the absolute difference between the predicted probability for the positive class and a 0-1 encoding of the ground truth and then average these values across the test set.
+  Test this with `classif.log_reg` on `tsk("sonar")`.
 
-The rules can easily be translated to R code where we expect `truth` and `prediction` to be factor vectors of the same length with levels `"A"` and `"B"`:
+The rules can easily be translated to R code where we first convert select the predicted probabilities for the positive class, 0-1 encode the truth vector and then calculate the mean absolute error between the two vectors.
 
 ```{r solutions-023}
-costsens = function(truth, prediction) {
-    score = numeric(length(truth))
-    score[truth == "A" & prediction == "B"] = 10
-    score[truth == "B" & prediction == "A"] = 1
-
-    mean(score)
+mae_prob = function(truth, prob, task) {
+  # retrieve positive class from task
+  positive = task$positive
+  # select positive class probabilities
+  prob_positive = prob[, positive]
+  # obtain 0-1 encoding of truth
+  y = as.integer(truth == positive)
+  # average the absolute difference
+  mean(abs(prob_positive - y))
 }
 ```
 
 This function can be embedded in the `Measure` class accordingly.
 
 ```{r solutions-024}
-MeasureCustom = R6::R6Class("MeasureCustom",
+MeasureMaeProb = R6::R6Class("MeasureMaeProb",
   inherit = mlr3::MeasureClassif, # classification measure
   public = list(
     initialize = function() { # initialize class
-      super$initialize(
-        id = "custom", # unique ID
+      super$initialize( # initialize method of parent class
+        id = "mae_prob", # unique ID
         packages = character(), # no dependencies
-        properties = character(), # no special properties
-        predict_type = "response", # measures response prediction
-        range = c(0, Inf), # results in values between (0, 1)
+        properties = "requires_task", # needs access to task for positive class
+        predict_type = "prob", # measures probability prediction
+        range = c(0, 1), # results in values between [0, 1]
         minimize = TRUE # smaller values are better
       )
     }
   ),
 
   private = list(
-    .score = function(prediction, ...) { # define score as private method
-      # define loss
-      costsens = function(truth, prediction) {
-        score = numeric(length(truth))
-        score[truth == "A" & prediction == "B"] = 10
-        score[truth == "B" & prediction == "A"] = 1
-
-        mean(score)
-      }
-
+    .score = function(prediction, task, ...) { # define score as private method
       # call loss function
-      costsens(prediction$truth, prediction$response)
+      mae_prob(prediction$truth, prediction$prob, task)
     }
   )
 )
 ```
 
-An alternative (as pointed to by the hint) can be constructed by first translating the rules to a matrix of misclassification costs, and then feeding this matrix to the constructor of `msr("classif.costs")`:
+Because this is a custom class that is not available in the `mlr_measures` dictionary, we have to create a new instance using the `$new()` constructor.
+
+```{r}
+msr_mae_prob = MeasureMaeProb$new()
+msr_mae_prob
+```
 
-```{r solutions-025}
-# truth in columns, prediction in rows
-C = matrix(c(0, 10, 1, 0), nrow = 2)
-rownames(C) = colnames(C) = c("A", "B")
-C
 
-msr("classif.costs", costs = C)
+To try this measure, we resample a logistic regression on the sonar task using five-fold cross-validation.
+
+```{r}
+# predict_type is set to "prob", as otherwise our measure does not work
+learner = lrn("classif.log_reg", predict_type = "prob")
+task = tsk("sonar")
+rr = resample(task, learner, rsmp("cv", folds = 5))
+```
+
+We now score the resample result using our custom measure and `msr("classif.acc")`.
+
+```{r}
+score = rr$score(list(msr_mae_prob, msr("classif.acc")))
+```
+
+In this case, there is a clear relationship between the classification accuracy and our custom measure, i.e. the higher the accuracy, the lower the mean absolute error of the predicted probabilities.
+
+```{r}
+cor(score$mae_prob, score$classif.acc)
+```
+
+
+3. "Tune" the `error_train` hyperparameter of the `classif.debug` learner on a continuous interval from 0 to 1, using a simple classification tree as the fallback learner and the penguins task.
+  Tune for 50 iterations using random search and 10-fold cross-validation.
+  Inspect the resulting archive and find out which evaluations resulted in an error, and which did not.
+  Now do the same in the interval 0.3 to 0.7.
+  Are your results surprising?
+
+First, we create the learner that we want to tune, mark the relevant parameter for tuning and set the fallback learner to a classification tree.
+
+```{r}
+lrn_debug = lrn("classif.debug",
+  error_train = to_tune(0, 1),
+  fallback = lrn("classif.rpart")
+)
+lrn_debug
+```
+
+This example is unusual, because we expect better results from the fallback classification tree than from the primary debug learner, which predicts the mode of the target distribution.
+Nonetheless it serves as a good example to illustrate the effects of training errors on the tuning results.
+
+We proceed with optimizing the classification accuracy of the learner on the penguins task.
+
+```{r}
+instance = tune(
+  learner =  lrn_debug,
+  task = tsk("penguins"),
+  resampling = rsmp("cv"),
+  tuner = tnr("random_search"),
+  measure = msr("classif.acc"),
+  term_evals = 50
+)
+instance
+```
+
+To find out which evaluations resulted in an error, we can inspect the `$archive` slot of the instance, which we convert to a `data.table` for easier filtering.
+
+```{r}
+archive = as.data.table(instance$archive)
+archive[, c("error_train", "classif.acc", "errors")]
+```
+
+Below, we visualize the relationship between the error probabilty and the classification accuracy.
+
+```{r}
+ggplot(data = archive, aes(x = error_train, y = classif.acc, color = errors)) +
+  geom_point() +
+  theme_minimal()
+```
+
+Higher values for `error_train` lead to more resampling iterations using the classification tree fallback learner and therefore to better classification accuracies.
+Therefore, the best found hyperparameter configurations will tend to have values of `error_train` close to 1.
+When multiple parameter configurations have the same test performance, the first one is chosen by `$result_learner_param_vals`.
+
+```{r}
+instance$result_learner_param_vals
+```
+
+We repeat the same experiment for the tuning interval from 0.3 to 0.7.
+
+```{r}
+lrn_debug$param_set$set_values(
+  error_train = to_tune(0.3, 0.7)
+)
+
+instance2 = tune(
+  learner =  lrn_debug,
+  task = tsk("penguins"),
+  resampling = rsmp("cv"),
+  tuner = tnr("random_search"),
+  measure = msr("classif.acc"),
+  term_evals = 50
+)
+
+archive2 = as.data.table(instance2$archive)
+instance2
+```
+
+As before, higher error probabilities during training lead to higher classification accuracies.
+
+```{r}
+ggplot(data = archive2, aes(x = error_train, y = classif.acc, color = errors)) +
+  geom_point() +
+  theme_minimal()
+```
+
+However, the best found configurations for the `error_train` parameter, now tend to be close to 0.7 instead of 1 as before.
+
+```{r}
+instance2$result_learner_param_vals
 ```
 
+This demonstrates that when utilizing a fallback learner, the tuning results are influenced not only by the direct impact of the tuning parameters on the primary learner but also by their effect on its error probability.
+Therefore, it is always advisable to manually inspect the tuning results afterward.
+Note that in most real-world scenarios, the fallback learner performs worse than the primary learner, and thus the effects illustrated here are usually reversed.
 
 ## Solutions to @sec-large-benchmarking
 
diff --git a/book/chapters/chapter10/advanced_technical_aspects_of_mlr3.qmd b/book/chapters/chapter10/advanced_technical_aspects_of_mlr3.qmd
index 14fa16f46..6ddac2bf6 100644
--- a/book/chapters/chapter10/advanced_technical_aspects_of_mlr3.qmd
+++ b/book/chapters/chapter10/advanced_technical_aspects_of_mlr3.qmd
@@ -993,12 +993,12 @@ resample(tsk_penguins, lrn_debug, rsmp_cv6)
 (b) Prove your point by measuring the elapsed time, e.g., using `r ref("system.time()")`.
 (c) What would you change in the setup and why?
 
-2. Create a new custom binary classification measure (either using methods demonstrated in @sec-extending or with `msr("classif.costs")`) which scores ("prob"-type) predictions.
+2. Create a new custom binary classification measure which scores ("prob"-type) predictions.
   This measure should compute the absolute difference between the predicted probability for the positive class and a 0-1 encoding of the ground truth and then average these values across the test set.
   Test this with `classif.log_reg` on `tsk(“sonar”)`.
 
-3. "Tune" the `error_train` hyperparameter of the `classif.debug` learner on a continuous interval from 0 to 1, using a simple fallback learner.
-  Tune for 50 iterations using random search and holdout resampling.
+3. "Tune" the `error_train` hyperparameter of the `classif.debug` learner on a continuous interval from 0 to 1, using a simple classification tree as the fallback learner and the penguins task.
+  Tune for 50 iterations using random search and 10-fold cross-validation.
   Inspect the resulting archive and find out which evaluations resulted in an error, and which did not.
   Now do the same in the interval 0.3 to 0.7.
   Are your results surprising?