Homework 4B
Machine Learning Assignment
Assigned 11/28/01

Notes:

	You may work in teams of up to three people for this assignment. You may
	*not* work with anyone that you have previously worked with during this
	class.

	Due Date: Monday December 17th

Assignment:

	1. Write a Naive Bayes classifier. Look at the format of the data sets
	   before you start programming.
	2. Download and get C4.5 working.
	   (http://www.cse.unsw.edu.au/~quinlan/c4.5r8.tar.gz)

	3. Download the following datasets from the UCI machine learning website
	   (http://www1.ics.uci.edu/~mlearn/MLSummary.html):
		
		Chess Endgames(king-rook vs king-pawn)
		Congressional Voting Records
		Echocardiogram
		Hepatitis
		Horse Colic
		Hypothyroid
		Labor Relations
		Lung Cancer
		Post-operative Patient
		Promoters Gene Sequencers
		Sonar
		Soybean

	4. Perform a 10-fold cross-validation accuracy study of the performance of
	your Naive Bayes classifier and Quinlan's C4.5 decision tree
	implementation.  For each of the datasets above report the mean and
	standard deviation of each machine learning algorithm across each fold.

	Do not differentiate between different types of errors (for example false
	positive and false negative) Discretize any continuous attributes into 10
	equal sized bins before training/testing your machine learners.  Make sure
	you include the -s option when you run C4.5.

	5. Create two additional datasets with at least 100 examples.  Using the
	same procedures as above, demonstrate that your Naive Bayes implementation
	algorithm is more accurate on the first data set and that C4.5 is more
	accurate on the second data set.  If the mean accuracy plus or minus one
	standard deviation overlaps between the two machine learners on one data
	set, in addition to the mean and standard deviation, demonstrate the
	statistical significance of your results using an appropriate test. (for
	example a paired t-test or paired Wilcoxon signed rank test)

	6. Turn-in a hard-copy of the table of results from 2, a discussion of the
	characteristics of the datasets in part 5, and a cover sheet identifying
	the group members, their email addresses and signatures acknowledging that
	this is your group's independent work.