anchor.diff:    * `validationTask`: this task finally evaluates the classifier on _testSet_.
     * `batchTask`: this tasks runs all the four task above.
   
  -#### Cross-validation dimension
  +### Cross-validation dimension
   
   With such a setup, the data which is the input for the cross validation is unknown, until the `preprocessingTask` has completed. Only when it is done, the n-fold cross validation can be set up during which part of the data is assigned to the _trainingSet_ and _testSet_.
   
  
positive.code: };
  ----
  
  #### Caveat
  
  Mind, that it is currently not tested to import data across batch task boundaries. That is, in the example above, the `featureExtractionTask` does not directly import data from the `preprocessingTask`. Instead, the `crossvalidationTask` imports the data from the `preprocessingTask` and forwards it to the `featureExtractionTask` via the file names in the fold dimension.
  
  #### Summary
  
  If a cross-validation task depends on the output of a preprocessing task, it is impossible to set up a static parameter dimension for the _trainingSet_ and _testSet_, because it depends on the data created by the preprocessing task. The tutorial has illustrated how to create a nested batch task which dynamically creates its own parameter space using a `FoldDimensionBundle` based on the output of the preprocessing task.
negative.code:     
  ## Cross validation
  
  This is a brief introduction to running an experiment that has a cross validation part. The most important things to learn in this example is, how to set a parameter space dimension which is created dynamically via a nested `BatchTask` and how to use a `FoldDimensionBundle`.
  
  ### Basic setup
  
  Assume you have these tasks:
  
  