[Chapter 9] k-Nearest Neighbors for Classification In the k-Nearest Neighbors method for classification, you set k = 1 to 20 and the data mining procedure reported the best k = 10. The table below gives the 10 nearest neighbors in the training set for a new observation and the class membership for each neighbor. Neighbor 2 3 4 5 6 Class 0 1 1 0 1 Answer the following questions based on the above information. 1 1 O Cannot be determined; more information is needed. Class 1 Question 33 If the cutoff value is 0.50, the new observation should be classified as Class 0 7 0 8 1 9 0 10 1
[Chapter 9] k-Nearest Neighbors for Classification In the k-Nearest Neighbors method for classification, you set k = 1 to 20 and the data mining procedure reported the best k = 10. The table below gives the 10 nearest neighbors in the training set for a new observation and the class membership for each neighbor. Neighbor 2 3 4 5 6 Class 0 1 1 0 1 Answer the following questions based on the above information. 1 1 O Cannot be determined; more information is needed. Class 1 Question 33 If the cutoff value is 0.50, the new observation should be classified as Class 0 7 0 8 1 9 0 10 1
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![[Chapter 9] k-Nearest Neighbors for Classification
In the k-Nearest Neighbors method for classification, you set k = 1 to 20 and the
data mining procedure reported the best k = 10.
The table below gives the 10 nearest neighbors in the training set for a new
observation and the class membership for each neighbor.
Neighbor
2
3
5
Class
0
1
1
0
Answer the following questions based on the above information.
1
1
Class 1
4
Class O
6
1
Cannot be determined; more information is needed.
Question 33
If the cutoff value is 0.50, the new observation should be classified as
7
0
8
1
9
0
10
1](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F88d95438-7803-4319-839d-625f1dc2e2ea%2F91442ba2-f9bd-49b0-be8b-56dbf63ee065%2Fwctxify.png&w=3840&q=75)
Transcribed Image Text:[Chapter 9] k-Nearest Neighbors for Classification
In the k-Nearest Neighbors method for classification, you set k = 1 to 20 and the
data mining procedure reported the best k = 10.
The table below gives the 10 nearest neighbors in the training set for a new
observation and the class membership for each neighbor.
Neighbor
2
3
5
Class
0
1
1
0
Answer the following questions based on the above information.
1
1
Class 1
4
Class O
6
1
Cannot be determined; more information is needed.
Question 33
If the cutoff value is 0.50, the new observation should be classified as
7
0
8
1
9
0
10
1
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