[Readings]

• Read Chapter 2.6.1, 5.4, 8.3.1, 8.3.2 (up to GSP), 6.1-6.3 of the textbook.

[Decision Tree Induction] (90pt)

For this assignment, you are going to implement a decision tree classifier as described in Chapter 6.3 of the textbook. You may use any programming language of your choice, as long as your program can be compiled and run on CS3.

java DecisionTreeClassifier <TrainingSet> <TestSet> <ResultSet>

• TrainingSet is the name of a text file that contains the records for training the classifier. Note that each record occupies one line in the data file, the attributes of each record are separated by comma, and the last attribute is the class label.
• TestSet contains the testing records in the same format as the training set. Note that the class labels of the records are included in the test set so you can calculate the accuracy rate of your classifier, but you may not use these class labels for any other purpose.
• ResultSet should be the same as the test set except that the class labels are replaced by the ones predicted by your classifier.

Your classifier should also output to the console the percentage of the correctly classified records, e.g. 50%. Please do not output any debugging information in the submitted code.

I will grade your implementation by randomly selecting 2100 records (300 records from each class) from the dataset as the testing set, and using the remaining records as the training set. Your implementation must achieve at least 30% accuracy rate, and take less than 2 hours to complete both training and classification on CS3. The top five most accurate classifiers will receive up to 20% extra credit.

Note that use of existing classification code found online or from other sources will be considered cheating.