[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 software 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. 1 1 2 0 Neighbor 5 Class 0 Answer the following questions based on the above information. oversampling standard partitioning overfitting undersampling standard sampling 3 1 The best kachieves the smallest 4 class 0 error rate overall error rate average error class 1 error rate root mean squared error 6 1 1 7 0 In the data partitioning procedure, if a rare event is involved in classifying a categorical outcome, then should be used for the training set. 8 1 on the validation set. 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 software 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. 1 1 2 0 Neighbor 5 Class 0 Answer the following questions based on the above information. oversampling standard partitioning overfitting undersampling standard sampling 3 1 The best kachieves the smallest 4 class 0 error rate overall error rate average error class 1 error rate root mean squared error 6 1 1 7 0 In the data partitioning procedure, if a rare event is involved in classifying a categorical outcome, then should be used for the training set. 8 1 on the validation set. 9 0 10 1
Computer Networking: A Top-Down Approach (7th Edition)
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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 software 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
Class
0
1
1
Answer the following questions based on the above information.
1
1
oversampling
standard partitioning
overfitting
undersampling
standard sampling
The best kachieves the smallest
5
0
class 0 error rate
overall error rate
6
average error
class 1 error rate
root mean squared error
7
0
1
In the data partitioning procedure, if a rare event is involved in classifying
a categorical outcome, then
should be used for the training set.
8
1
on the validation set.
9
0
10
1](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3306ea0d-0f79-489f-b4ee-38af79ef2243%2F9ae0ec09-eec2-4aa2-bbf6-ad8528a09d7b%2Ffiidds.jpeg&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 software 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
Class
0
1
1
Answer the following questions based on the above information.
1
1
oversampling
standard partitioning
overfitting
undersampling
standard sampling
The best kachieves the smallest
5
0
class 0 error rate
overall error rate
6
average error
class 1 error rate
root mean squared error
7
0
1
In the data partitioning procedure, if a rare event is involved in classifying
a categorical outcome, then
should be used for the training set.
8
1
on the validation set.
9
0
10
1
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