Computer Science Consider two lift charts below, which represent two different classification problems. Chart A shows the rate of success in finding customers who are predicted to repay a loan on time. Chart B involves finding customers who will accept a loan offer. Lift chart A 800 700 600
Computer Science Consider two lift charts below, which represent two different classification problems. Chart A shows the rate of success in finding customers who are predicted to repay a loan on time. Chart B involves finding customers who will accept a loan offer. Lift chart A 800 700 600
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
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Question
![Computer Science
Consider two lift charts below, which represent two different classification problems. Chart A shows
the rate of success in finding customers who are predicted to repay a loan on time. Chart B involves
finding customers who will accept a loan offer.
Lift chart A
800
700
600
500
Cumulat ive RESPONSE
when sorted using
predicted values
400
300
Cumulat ive RESPONSE
200
using average
100
500
1000
1500
# Cases
Lift Chart B
600
s00
400
Cumulat ive Personal
Loan when sorted
300
using predicted values
200
Cumulat ive Personal
Loan using average
100
2000
4000
6000
Cases
What can you say about the benefit of using analytics in these two cases from the lift charts? E.g, in
which chart is analytics performing better? Why? How well could you do without analytics in the two
cases?
Cumuative
Cemubtive](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fbf6c446e-50c8-4d83-a77c-d383b505b1f1%2F256ed2a2-e544-404b-bb49-3b7941048528%2Fg4jeinu_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Computer Science
Consider two lift charts below, which represent two different classification problems. Chart A shows
the rate of success in finding customers who are predicted to repay a loan on time. Chart B involves
finding customers who will accept a loan offer.
Lift chart A
800
700
600
500
Cumulat ive RESPONSE
when sorted using
predicted values
400
300
Cumulat ive RESPONSE
200
using average
100
500
1000
1500
# Cases
Lift Chart B
600
s00
400
Cumulat ive Personal
Loan when sorted
300
using predicted values
200
Cumulat ive Personal
Loan using average
100
2000
4000
6000
Cases
What can you say about the benefit of using analytics in these two cases from the lift charts? E.g, in
which chart is analytics performing better? Why? How well could you do without analytics in the two
cases?
Cumuative
Cemubtive
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