Object number Home Defaulted borrower Marital status Sex Income owner 1 Yes Single Female 150 No 2 Yes Married Female 210 Yes 3 Yes Divorced Male 85 No 4 No Married Female 80 Yes 5 Yes Single Male 110 Yes 6 No 7 Yes Divorced Female 85 Yes Single Female 90 Yes 8 No Married Female 55 No 9 No Divorced Male 85 No 10 Yes Married Male 95 No - Uslng the constructed tree, predict a class for the following unknown object - Predict a class for the following record using Nalve Bayes Classifier Object number Home Marital status Sex Income Defaulted 1 Yes Single 80 ?? Female
Object number Home Defaulted borrower Marital status Sex Income owner 1 Yes Single Female 150 No 2 Yes Married Female 210 Yes 3 Yes Divorced Male 85 No 4 No Married Female 80 Yes 5 Yes Single Male 110 Yes 6 No 7 Yes Divorced Female 85 Yes Single Female 90 Yes 8 No Married Female 55 No 9 No Divorced Male 85 No 10 Yes Married Male 95 No - Uslng the constructed tree, predict a class for the following unknown object - Predict a class for the following record using Nalve Bayes Classifier Object number Home Marital status Sex Income Defaulted 1 Yes Single 80 ?? Female
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
Related questions
Question
![Home
Defaulted
Object number
Marital status
Sex
Income
owner
borrower
1 Yes
Single
Female
150 No
2 Yes
Married
Female
210 Yes
3 Yes
Divorced
Male
85 No
4 No
Married
Female
80 Yes
5 Yes
Single
Male
110 Yes
6 No
7 Yes
Divorced
Female
85 Yes
Single
Female
90 Yes
8 No
Married
Female
55 No
9 No
Divorced
Male
85 No
10 Yes
Married
Male
95 No
- Using the constructed tree, predict a class for the following unknown object
- Predict a class for the followlng record uslng Nalve Bayes Classifier
Object number Home
1 Yes
Marital status
Sex
Income
Defaulted
Single
Female
80 ??
THIS IS MY 4TH TIME POSTING THIS, PLEASE ANSWER IF YOU KNOW ITS RIGHT. MACHINE
LEARNING PROBLEM](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F6c5f6cdf-1ba2-4fc9-bc1b-fca1ab5fd44b%2F192f33cb-33d0-49d3-965d-3b2a50037668%2Fslmxwj7_processed.png&w=3840&q=75)
Transcribed Image Text:Home
Defaulted
Object number
Marital status
Sex
Income
owner
borrower
1 Yes
Single
Female
150 No
2 Yes
Married
Female
210 Yes
3 Yes
Divorced
Male
85 No
4 No
Married
Female
80 Yes
5 Yes
Single
Male
110 Yes
6 No
7 Yes
Divorced
Female
85 Yes
Single
Female
90 Yes
8 No
Married
Female
55 No
9 No
Divorced
Male
85 No
10 Yes
Married
Male
95 No
- Using the constructed tree, predict a class for the following unknown object
- Predict a class for the followlng record uslng Nalve Bayes Classifier
Object number Home
1 Yes
Marital status
Sex
Income
Defaulted
Single
Female
80 ??
THIS IS MY 4TH TIME POSTING THIS, PLEASE ANSWER IF YOU KNOW ITS RIGHT. MACHINE
LEARNING PROBLEM
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