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:    * `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:     
  ## 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:
  
  