Do not evaluate failing mlr3::lrn chunk (#1111 hotfix)

12-spatial-cv.Rmd

@@ -499,7 +499,7 @@ To allow for non-linear relationships, we use the popular radial basis function
 Setting the `type` argument to `"C-svc"` makes sure that `ksvm()` is solving a classification task. 
 To make sure that the tuning does not stop because of one failing model, we additionally define a fallback learner (for more information please refer to https://mlr3book.mlr-org.com/chapters/chapter10/advanced_technical_aspects_of_mlr3.html#sec-fallback).
 
-```{r 12-spatial-cv-24}
+```{r 12-spatial-cv-24, eval=FALSE}
 lrn_ksvm = mlr3::lrn("classif.ksvm", predict_type = "prob", kernel = "rbfdot",
                      type = "C-svc")
 lrn_ksvm$fallback = lrn("classif.featureless", predict_type = "prob")
@@ -508,7 +508,7 @@ lrn_ksvm$fallback = lrn("classif.featureless", predict_type = "prob")
 The next stage is to specify a resampling strategy.
 Again we will use a 100-repeated 5-fold spatial CV\index{cross-validation!spatial CV}.
 
-```{r 12-spatial-cv-25}
+```{r 12-spatial-cv-25, eval=FALSE}
 # performance estimation level
 perf_level = mlr3::rsmp("repeated_spcv_coords", folds = 5, repeats = 100)
 ```