diff --git a/12-spatial-cv.Rmd b/12-spatial-cv.Rmd
index 98f0374b5..755da8bdf 100644
--- a/12-spatial-cv.Rmd
+++ b/12-spatial-cv.Rmd
@@ -565,12 +565,11 @@ Repeated 100 times means fitting a total of 125,000 models to identify optimal h
 These are used in the performance estimation, which requires the fitting of another 500 models (5 folds \* 100 repetitions; see Figure \@ref(fig:partitioning)). 
 To make the performance estimation processing chain even clearer, let us write down the commands we have given to the computer:
 
-1. Performance level (upper left part of Figure \@ref(fig:inner-outer)) - split the dataset into five spatially disjoint (outer) subfolds
-1. Tuning level (lower left part of Figure \@ref(fig:inner-outer)) - use the first fold of the performance level and split it again spatially into five (inner) subfolds for the hyperparameter tuning. 
-Use the 50 randomly selected hyperparameters\index{hyperparameter} in each of these inner subfolds, i.e., fit 250 models
-1. Performance estimation: use the best hyperparameter combination from the previous step (tuning level) and apply it to the first outer fold in the performance level to estimate the performance (AUROC\index{AUROC})
-1. Repeat steps 2 and 3 for the remaining four outer folds
-1. Repeat steps 2 to 4, 100 times
+1. Performance level (upper left part of Figure \@ref(fig:inner-outer)): split the dataset into five spatially disjoint (outer) subfolds.
+1. Tuning level (lower left part of Figure \@ref(fig:inner-outer)): use the first fold of the performance level and split it again spatially into five (inner) subfolds for the hyperparameter tuning. Use the 50 randomly selected hyperparameters\index{hyperparameter} in each of these inner subfolds, i.e., fit 250 models.
+1. Performance estimation: use the best hyperparameter combination from the previous step (tuning level) and apply it to the first outer fold in the performance level to estimate the performance (AUROC\index{AUROC}).
+1. Repeat steps 2 and 3 for the remaining four outer folds.
+1. Repeat steps 2 to 4, 100 times.
 
 The process of hyperparameter tuning and performance estimation is computationally intensive.
 To decrease model runtime, **mlr3** offers the possibility to use parallelization\index{parallelization} with the help of the **future** package.