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Question 3: Ali has lost information of one of her customers, and does not know whether to make a skirt or trousers. Can you help him to make a better decision using a KNN-classifier? The customer who is missing gender infomation: Gender -- Waist 28, Hip 34. Use K=2 nearest neighbors. Fill in the table to calculate KNN the Euclidean distance Gender Waist (cm) Hip (cm) 32 35 Distance Male Male 28 33 27 31 Female 33 Female 36 A. Count of the male neighborhood members: B. Count of the female neighborhood members: C. The probably Gender is:

Question 3: Ali has lost information of one of her customers, and does not know whether to make a skirt or trousers. Can you help him to make a better decision using a KNN-classifier? The customer who is missing gender infomation: Gender -- Waist 28, Hip 34. Use K=2 nearest neighbors. Fill in the table to calculate KNN the Euclidean distance Gender Waist (cm) Hip (cm) 32 35 Distance Male Male 28 33 27 31 Female 33 Female 36 A. Count of the male neighborhood members: B. Count of the female neighborhood members: C. The probably Gender is:

Computer Networking: A Top-Down Approach (7th Edition)
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...
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Question 3: Ali has lost information of one of her customers, and does not know whether to make a skirt or trousers. Can you help him to make a better decision using a KNN-classifier? The customer who is missing gender information: Gender --?--, Waist 28, Hip 34, Use K=2 nearest neighbors. Fill in the table to calculate KNN the Euclidean distance. ITT Distance Gender Waist (cm) Hip (cm) Male Male Female Female 28 32 33 27 31 35 33 36 A. Count of the male neighborhood members: B. Count of the female neighborhood members: C. The probably Gender is:
Transcribed Image Text:Question 3: Ali has lost information of one of her customers, and does not know whether to make a skirt or trousers. Can you help him to make a better decision using a KNN-classifier? The customer who is missing gender information: Gender --?--, Waist 28, Hip 34, Use K=2 nearest neighbors. Fill in the table to calculate KNN the Euclidean distance. ITT Distance Gender Waist (cm) Hip (cm) Male Male Female Female 28 32 33 27 31 35 33 36 A. Count of the male neighborhood members: B. Count of the female neighborhood members: C. The probably Gender is:
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