hw2 - CS446 Machine Learning Fall 2012 Problem Set 2 Handed...

CS446: Machine Learning Fall 2012
Problem Set 2
Handed Out: September 17 2014 Due: September 25, 2014
• Feel free to talk to other members of the class in doing the homework. I am more concerned that
you learn how to solve the problem than that you demonstrate that you solved it entirely on your
own. You should, however, write down your solution yourself. Please try to keep the solution brief
and clear.
• Please use Piazza first if you have questions about the homework. Also feel free to send us e-mails
and come to office hours.
• Please, no handwritten solutions. You will submit your solution manuscript as a single pdf file.
• A large portion of this assignment deals with programming decision trees to see them in action. While
we do provide several pieces of code, you are required to try and test several decision tree algorithms
by writing your own code. While we encourage discussion within and outside the class, cheating and
copying code is strictly prohibited. Copied code will result in the entire assignment being discarded
at the very least .
• The homework is due at 11:59 PM on the due date. We will be using Compass for collecting
the homework assignments. Please submit your solution manuscript as a pdf file via Compass
( ). Please do NOT hand in a hard copy of your write-up. Contact
the TAs if you are having technical difficulties in submitting the assignment.
1. Learning Decision Trees – 20 points
For this question, you will manually induce a decision tree from a small data set.
Table 1 shows the Balloons data set from the UCI Machine Learning repository that
was first used for an experiment in cognitive psychology 1 . The data consists of four
attributes ( Color , Size , Act , and Age ) and a binary label ( Inflated ). You will
represent this data as decision trees using two splitting heuristics.
(a) [7 points] Use the ID3 heuristic to represent the data as a decision tree.
You can report the decision tree as a series of if-then statements in the text, or
graphically, it’s your choice.
Example:
if feature_0 = x :
if feature_1 = y :
class = T
else :
class = F
else:
if feature ...
...
1
You can learn more about this data set at
1

Color Size Act Age Inflated
Yellow Small Stretch Adult T
Yellow Small Stretch Child T
Yellow Small Dip Adult T
Yellow Small Dip Child T
Yellow Large Stretch Adult T
Yellow Large Stretch Child F
Yellow Large Dip Adult F
Yellow Large Dip Child F
Purple Small Stretch Adult T
Purple Small Stretch Child F
Purple Small Dip Adult F
Purple Small Dip Child F
Purple Large Stretch Adult T
Purple Large Stretch Child F
Purple Large Dip Adult F
Purple Large Dip Child F