I need help with question #2 in Part 1. I've written the question below but I have also attached the homework assignment.

Spam has become an increasingly annoying problem for e-mail users. In this problem weare interested in using the ID3 decision tree induction algorithm to automatically determinewhether or not an e-mail is a spam based on whether the words “nigeria”, “viagra”, and“learning” appear in the e-mail. Below are the instances from which our decision tree willbe learned. Note that a word has the value 1 if and only if it is present in the correspondinge-mail.

Using these descriptions as training instances, show the decision tree created by the ID3decision tree learning algorithm. Show the information gain calculations that you computedto create the tree. Be sure to indicate the class value to associate with each leaf of the treeand the set of instances that are associated with each leaf.

CS 4375 Introduction to Machine LearningFall 2016Assignment 1: Decision Tree InductionPart I:Due electronically by Tuesday, September 6, 11:59 p.m.Part II:Due electronically by Tuesday, September 13, 11:59 p.m.Note:1. Your solution to this assignment must be submitted via eLearning.2. Whenever possible, you should provide brief justiFcations for your solution.3. You may work in a group of two or individually.Part I: Written Problems (25 points)1.Representing Boolean Functions (10 points)Give decision trees to represent the following concepts:(a) (¬A∨B)∧ ¬(C∧A). Your decision tree must contain as few nodes as possible.(b) (A⊕B)∧C2.Decision Trees (15 points)Spam has become an increasingly annoying problem for e-mail users. In this problem weare interested in using the ID3 decision tree induction algorithm to automatically determinewhether or not an e-mail is a spam based on whether the words “nigeria”, “viagra”, and“learning” appear in the e-mail. Below are the instances from which our decision tree willbe learned. Note that a word has the value 1 if and only if it is present in the correspondinge-mail.No.nigeriaviagralearningClass110012001130000411005000061011701108100190000101001Using these descriptions as training instances, show the decision tree created by the ID3decision tree learning algorithm. Show the information gain calculations that you computedto create the tree. Be sure to indicate the class value to associate with each leaf of the treeand the set of instances that are associated with each leaf.1

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