reference spatial cv chapter and mlr3 book

15-eco.Rmd

@@ -519,7 +519,7 @@ search_space = paradox::ps(
 Having defined the search space, we are all set for specifying our tuning via the `AutoTuner()` function.
 Since we deal with geographic data, we will again make use of spatial cross-validation to tune the hyperparameters\index{hyperparameter} (see Sections \@ref(intro-cv) and \@ref(spatial-cv-with-mlr)).
 Specifically, we will use a five-fold spatial partitioning with only one repetition (`rsmp()`). 
-In each of these spatial partitions, we run 50 models (`trm()`) while using randomly selected hyperparameter configurations (`tnr()`) within predefined limits (`seach_space`) to find the optimal hyperparameter\index{hyperparameter} combination.
+In each of these spatial partitions, we run 50 models (`trm()`) while using randomly selected hyperparameter configurations (`tnr()`) within predefined limits (`seach_space`) to find the optimal hyperparameter\index{hyperparameter} combination [see also Section \@ref(svm) and https://mlr3book.mlr-org.com/optimization.html#autotuner, @becker_mlr3_2021].
 The performance measure is the root mean squared error (RMSE\index{RMSE}).
 
 ```{r 15-eco-22, eval=FALSE}