anchor.diff:  };
   ----
   
  -#### Caveat
  +### 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
  +### 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.
positive.code:    * `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
  
  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.
  
  
negative.code:   * `validationTask`: this task finally evaluates the classifier on _testSet_.
    * `batchTask`: this tasks runs all the four task above.
  
  ### 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_.
  
  