====== MLWizard Command Line Interface ======

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MLWizard can be used via the command line, as well. It will be started using the provided jar-file:
<code>
java -cp /path/to/rapidminer.jar:/path/to/MLWizard.jar de.dfki.madm.mlwizard.cli.CommandLineInterface <dataset> <task> [outfile]
</code>

The first argument is the path to the dataset in [[http://weka.wikispaces.com/XRFF|XRFF-format]] that will be used as input.

The second argument is the task that should be performed and can be one of the following:
  * wizard       - runs the complete wizard
  * metafeatures - computes the meta-features
  * recommend    - recommends classifers
  * evaluate     - evaluates all classifiers
  * construct    - constructs the classification system

The third argument is optional and defines where the results are written to. The result depends on the task and might be meta-features, a parameter set or a RapidMiner pipeline. If no file is supplied, the results are written to stdout.

A sample session might be:

<code>
java -cp /path/to/rapidminer.jar:/path/to/MLWizard.jar de.dfki.madm.mlwizard.cli.CommandLineInterface iris.xrff wizard result.xml
[ 0] k-NN               0.87
[ 1] Rule Induction     0.87
[ 2] Neural Net         0.87
[ 3] SVM                0.86
[ 4] Decision Tree      0.84
[ 5] Naive Bayes        0.79
Enter comma separated numbers of classifiers you want to be evaluated:
0,1,2
[ 0] k-NN               0.97                                                                                        
          k-NN.weighted_vote           true                                                                         
[ 1] Rule Induction     0.96                                                                                        
          Rule Induction.criterion     information_gain                                                             
[ 2] Neural Net         0.98                                                                                        
          Neural Net.decay             false                                                                        
[ 3] SVM                <not evaluated>                                                                             
[ 4] Decision Tree      <not evaluated>                                                                             
[ 5] Naive Bayes        <not evaluated>                                                                             
Enter number of classifier you want a system for:
2
</code>
The created RapidMiner pipeline will be stored in result.xml and can be imported into Rapidminer.

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