A bank has a new potential customer seeking a personal loan. To help pre-screen their customers, they performed a k-Nearest Neighbor analysis to determine if the new customer is more similar to those customers who received a loan (indicated by a "Yes") or those whose applications were rejected ("No"). The 10 customers which most closely resembled the new customer are listed in the table provided. This table also includes the Euclidean distance betwee the new customer and the indicated prior applicant as well as the indicator whether or not that prior customer received a loan: Customer Applicant Number 1 2 3 4 5 6 7 8 9 10 O No Closeness with Received Loan? New Applicant 5.38 1.37 6.89 6.9 2.67 8.28 0.77 3.35 0.14 3.03 If the bank used k-Nearest Neighbor analysis when k=7, then how would the new potential customer be classified? Yes YES YES YES YES YES NO NO NO NO NO Need more information to decide
A bank has a new potential customer seeking a personal loan. To help pre-screen their customers, they performed a k-Nearest Neighbor analysis to determine if the new customer is more similar to those customers who received a loan (indicated by a "Yes") or those whose applications were rejected ("No"). The 10 customers which most closely resembled the new customer are listed in the table provided. This table also includes the Euclidean distance betwee the new customer and the indicated prior applicant as well as the indicator whether or not that prior customer received a loan: Customer Applicant Number 1 2 3 4 5 6 7 8 9 10 O No Closeness with Received Loan? New Applicant 5.38 1.37 6.89 6.9 2.67 8.28 0.77 3.35 0.14 3.03 If the bank used k-Nearest Neighbor analysis when k=7, then how would the new potential customer be classified? Yes YES YES YES YES YES NO NO NO NO NO Need more information to decide
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Transcribed Image Text:A bank has a new potential customer seeking a personal loan. To help pre-screen their
customers, they performed a k-Nearest Neighbor analysis to determine if the new customer is
more similar to those customers who received a loan (indicated by a "Yes") or those whose
applications were rejected ("No"). The 10 customers which most closely resembled the new
customer are listed in the table provided. This table also includes the Euclidean distance betwee
the new customer and the indicated prior applicant as well as the indicator whether or not that
prior customer received a loan:
Customer Applicant
Number
1
2
3
4
5
6
7
8
9
10
O No
Closeness with
Received Loan? New Applicant
5.38
1.37
6.89
6.9
2.67
8.28
0.77
3.35
0.14
3.03
Yes
YES
YES
YES
YES
YES
INO
NO
INO
INO
If the bank used k-Nearest Neighbor analysis when k=7, then how would the new potential
customer be classified?
NO
O Need more information to decide
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