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hmk2(learning1)

# hmk2(learning1) - 2 one without general majority voting i.e...

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CSE352 HOMEWORK 2 (Decision Tree Learning 1) – 15pts 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. one with general majority voting , as defined in lecture notes, i.e. majority voting at any node of your choice. Use CREDIT RATING as the root attribute, and nodes attributes of your choice;

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Unformatted text preview: 2. one without general majority voting; i.e use ID3 algorithm (without Information Gain). Use INCOME as root attribute, and nodes attributes of your choice; EVALUATE Predictive accuracy for each of your trees (sets of rules) – use the TEST Dataset below. 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 No 6 <=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 discriminant rules in the lecture notes L8 with respect of the TEST Dataset from Problem 1....
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hmk2(learning1) - 2 one without general majority voting i.e...

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