
In order to become familiar with the Algorithm::DecisionTree module:

  (1)    Run the 

               training_data_generator.pl

         script to create your training data. First run the
         script as it is, and then make a copy of the
         param.txt file, modify this parameter file as you
         wish, and run the above the script with your version of
         param.txt.


  (2)    Next run the 

                construct_dt_and_classify_one_sample.pl

         script as it is.  Now modify the test sample in
         this script and see what classification results you
         get for the new test sample.  Next run this script
         on the new training datafile that you yourself
         created.  You would obviously need to use the test
         samples that mention the feature and value names in
         your own parameter file.


  (3)    Now run the test data generator script by invoking 

                generate_test_data.pl

         As it is, it will put out 20 samples for testing. But you
         can set that number to anything you wish.

         The test data is dumped into a file without the class labels
         for obvious reasons.  The class labels are dumped into a
         separate file whose name you can specify in the above 
         script.  As currently programmed, the name of this file is

                test_data_class_labels.dat

         By comparing the class labels returned by the classifier 
         with the class labels in this file, you can assess the 
         accuracy of the classifier.


  (4)    Finally, run the classifier on the test datafile by

         classify_test_data_in_a_file.pl  training.dat  testdata2.dat  out.txt

         Note carefully the three arguments you must supply the script.
         The first is for where the training data is, the second for 
         where the test data is, and the last where the classification 
         results will be deposited.


