Dr. Eick
COSC 6342“Machine Learning” Assignment2 Spring 2009
First Draft
Due: Tuesday, April 21, 11p (electronic Submission); problem 9 is due Sa., April 25, 11p
8. I---Topic8
Construct a one-dimensional classification dataset for which the leave-one-out cross
validation error for 1NN is always 1—in other words, the 1NN algorithm never predicts
the held out example correctly.
9. G---Topic8 (don’t start too late solving this problem!!)
a) Download the arsenic dataset arsenic_ds1_D1.txt ignoring the class label!
b) Compute the average 5-nearest neighbor distance called d5 using Euclidian
distance
c) Compute and Visualize the Gaussian Kernel density function for 22=0.5*d5, d5,
22=d5, 22=d5*d5, 2 (see Topic8d.ppt)
d) Compute and Visualize the k-NN density function for k=3, k=5 and k=7 (see
Topic8d.ppt)
e) Analyze the differences in the 6 created density functions!
f) Explain the differences in the distance functions (try your best!)!
g) Submit a report that contains your software, visualizations, and answers to
questions e and f!
10. I---Topic 10
Assume the following dataset is given with two nominal attributes A and B, and 3
different classes C1, C2, and C3. Compute the information gain for A and B. Based on
your answers to the last question which test should be used as the root of a decision tree?
A B Class
1 2 C3
1 1 C3
1 2 C1
1 2 C1
2 2 C1
2 1 C2
3 1 C2
3 1 C2
3 1 C2
3 2 C2
11. I---Topic13+14
a) Support vector machine maximize margins when creating hyperplanes. What is the
motivation for doing that? Why a large margins desirable?
1
