Merge branch 'main' into proof_c14

12-spatial-cv.Rmd

@@ -274,7 +274,7 @@ It acts as a 'meta-package', providing a unified interface to popular supervised
 The standardized **mlr3** interface is based on eight 'building blocks'.
 As illustrated in Figure \@ref(fig:building-blocks), these have a clear order.
 
-(ref:building-blocks) Basic building blocks of the mlr3 package. @bischl_applied_2024. Permission to reuse this figure was kindly granted.
+(ref:building-blocks) Basic building blocks of the mlr3 package [@bischl_applied_2024]. Permission to reuse this figure was kindly granted.
 
 ```{r building-blocks, echo=FALSE, fig.height=4, fig.width=4, fig.cap="(ref:building-blocks)", fig.scap="Basic building blocks of the mlr3 package."}
 knitr::include_graphics("images/12_ml_abstraction_crop.png")
@@ -568,7 +568,7 @@ To make the performance estimation processing chain even clearer, let us write d
 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. 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