Data Science

Classification: Basic Concepts

Decision Tree Induction

Bayes Classification Methods

Rule-Based Classification

Assignment 4

1- Consider the data in the following table:

TID

Home Owner

Marital Status

Annual Income

Defaulted Borrower

1

Yes

Single

[120 – < 150K] No 2 No Married [90 - < 120K] No 3 No Single [60 - < 90K] No 4 Yes Married [120 - < 150K] No 5 No Divorced [90 - < 120K] Yes 6 No Married [60 - < 90K] No 7 Yes Divorced [120 - < 150K] No 8 No Single [90 - < 120K] Yes 9 No Married [60 - < 90K] No 10 No Single [90 - < 120K] Yes Let Defaulted Borrower be the class label attribute. a) Given a data tuple X = (Home Owner= No, Marital Status= Married, Income= $120K). What would a naive Bayesian classification of the Defaulted Borrower for the tuple be? 2- Consider the training example in the following table for a binary classification problem. Customer ID Gender Car Type Shirt Size Class 1 M Family S C0 2 M Sports M C0 3 M Sports M C0 4 M Sports L C0 5 M Sports XL C0 6 M Sports XL C0 7 F Sports S C0 8 F Sports S C0 9 F Sports M C0 10 F Luxury L C0 11 M Family L C1 12 M Family XL C1 13 M Family M C1 14 M Luxury XL C1 15 F Luxury S C1 16 F Luxury S C1 17 F Luxury M C1 18 F Luxury M C1 19 F Luxury M C1 20 F Luxury L C1 a) Find the gain for Gender, Car Type, and Shirt Size. b) Which attribute will be selected as the splitting attribute? attachment IT546Fall2020_Assignment4.doc


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