HOMEWORK EXAMPLE FOR DATA MINING (cse634) CSE352 HOMEWORK 4 (Decision Tree Learning) TRAINING DATA SET FOR THE HOMEWORK: Class Attribute: Buys Computer Age Income Student Credit Rating Buys Computer <=30 high No Fair No <=30 high No Excellent No 31…40 high No Fair Yes >40 medium No Fair Yes >40 Low Yes Fair Yes >40 low Yes Excellent No 31…40 low Yes Excellent Yes <=30 medium No Fair No <=30 low Yes Fair Yes >40 medium Yes Fair Yes <=30 medium Yes Excellent Yes 31…40 medium No Excellent Yes 31…40 high Yes Fair Yes >40 medium No Excellent No Problem 1 Use the Training Data to create two decision trees: 1. Tree 1 - with majority voting (if needed); 2. Tree 2 - with general majority voting , i.e. majority voting at any node of your choice. 3. For the tree 1. use INCOME as root attribute, and nodes attributes of your choice; 4. For the tree 2. use CREDIT RATING as the root attribute, and nodes attributes of your choice; TEST DATA SET Obj Age Income Student Credit_Rating Class 1 <=30 High Yes Fair Yes 2 31…40 Low No Fair Yes 3 31…40 High Yes Excellent No 4 >40 Low Yes Fair Yes 5 >40 Low Yes Excellent No6 <=30 Low No Fair No Problem 2 Create test data sets for your sets rules corresponding to trees 1. and 2. that guarantees 100% predictive accuracy. Problem 3 Compute the predictive accuracy of the set of rules in the lecture notes (with respect of the TEST Dataset from Problem 1. Problem 4 1. Create a classification data base of 20 records with 6 attributes (non- class) and 3 classes. At least 3 attributes must have continuous numerical values. 2. Create 2 sets of data D1 and D2 using 2 disctretization methods. 3. Create 2 classifiers based on D1 and D2, respectively. Describe each step in the process. Write justification of all methods you decided to use in the process. Problem 4 Learning with Neural Networks Given two records (Training Sample) a1 a2 a3 Class 0.5 0 0.2 1 0 0.3 0 1 1. Use the Network below to evaluate a passage of TWO EPOCHS. Learning Rate l = 0.7 312456w14 w15 w24 w25 w34 w35 w46 w56REMEMBER: YOU HAVE TO SET YOUR INITIAL WEIGHTS AND BIASES RANDOMLY; DON’T USE THE SET-UP FROM THE EXAMPLE. 2. Write you’re the terminating conditions for your network 3. Write a condition for success; i.e. how you decide that the record is well classified.
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