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CS446: Machine LearningFall 2012Problem Set 2Handed Out: September 172014Due: September 25,2014•Feel free to talk to other members of the class in doing the homework.I am more concerned thatyou learn how to solve the problem than that you demonstrate that you solved it entirely on yourown. You should, however, write down your solution yourself. Please try to keep the solution briefand clear.•Please use Piazza first if you have questions about the homework. Also feel free to send us e-mailsand 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. Whilewe do provide several pieces of code, you are required to try and test several decision tree algorithmsby writing your own code. While we encourage discussion within and outside the class,cheating andcopying code is strictly prohibited. Copied code will result in the entire assignment being discardedat the very least.•The homework is due at 11:59 PM on the due date.We will be using Compass for collectingthe 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. Contactthe TAs if you are having technical difficulties in submitting the assignment.1.Learning Decision Trees – 20 pointsFor this question, you will manually induce a decision tree from a small data set.Table 1 shows theBalloonsdata set from the UCI Machine Learning repository thatwas first used for an experiment in cognitive psychology1. The data consists of fourattributes (Color,Size,Act, andAge) and a binary label (Inflated).You willrepresent 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 ofif-thenstatements in the text, orgraphically, it’s your choice.Example:if feature_0 = x :if feature_1 = y :class = Telse :class = Felse:if feature ......1You can learn more about this data set at1