Due: September 19, 2008 (midnight)
For this assignment you will learn about the decision-tree induction
classifier and compare it to the ConjunctiveRule classifier.
- Exercise 3.4, page 77 of Mitchell's book.
- Use WEKA to run the J48 decision-tree classifier on the
contact-lenses dataset that comes with WEKA. Use the default
parameter settings for J48, and use the training set as the test
- Include in your report the printed results (tree and statistics)
- Draw graphically the decision tree classifier learned by J48.
- What is the percent accuracy of this tree on the training set?
- WEKA's default parameter settings for J48 are -C 0.25 -M 2.
- Explain in your own words what these parameters mean.
- Find a setting for the -C and -M parameters so that
the learned tree achieves 100% accuracy on the training set. Describe the
difference between this tree and the one learned in problem 1.
- Run the ConjunctiveRule and J48 classifiers using default parameter
settings on the following eight datasets supplied with WEKA (J48 does
not work with the cpu datasets, because their class value is a real
For each run, use the Percentage Split test option with 66%
training. Include in your report a table giving the percent correctly
classified instances in the test split for both classifiers on each
- Compare the performance of the ConjunctiveRule and J48 classifiers
based on the results from the previous problem.
Specifically, which classifier performs better on which datasets and why.
The "why" part should consider the characteristics of the data, the
hypothesis space, and the learning algorithm.
- Email to me (email@example.com)
your nicely-formatted report (MSWord, PDF or PostScript) containing the
information referred to above.