anchor.diff:     * `crossValidationTask`: this task runs the `featureExtractionTask`, the `trainingTask` and the `validationTask`
      * `batchTask` (modified): this tasks now runs the `preprocessingTask` and the `crossValidationTask`.
   
  -#### Cross-validation task
  +### Cross-validation task
   
   Now, instead of statically setting up the parameter space for the `crossValidationTask`, we set it up dynamically by overriding the `execute()` method. In the following example, we assume that the `preprocessingTask` has produced some `XMI` data, which is the data on which we want to perform the cross-validation. The `featureExtractionTask` requires two parameters, `filesRoot` (the base directory of the data) and `files_training` (the data in the _trainingSet_). The `validationTask`also requires `filesRoot` and it needs `files_validation` (the _testSet_ ) to evaluate the classifier.
   
  
positive.code:     
  ## Cross validation
  
  ### Cross validation tutorial
  
  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 tasks
  
  Assume you have these tasks:
  
  
negative.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.