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	1	2	3	4	5	6	7	8	9	10
Class	1	0	0	1	0	1	0	1	0	0

Answer the following questions based on the above information.

Question 31

In the data partitioning procedure, if a rare event is involved in classifying a categorical outcome, then __________ should be used for the training set.

Question 31 options:

	standard partitioning
	oversampling
	overfitting
	standard sampling
	undersampling

Question 33A (3 points)

For the new observation, the probability of being in Class 1 = __________.

Question 33A options:

	0.6
	0.8
	0.4
	0.7
	0.1
	0.5
	0.9
	0.3
	0.2
	1

Question 33 (3 points)

If the cutoff value is 0.50, the new observation should be classified as __________.

Question 33B options:

	Cannot be determined; more information is needed.
	Class 1
	Class 0