For k = 4, Observation 7 should be classified by k-NN in a. Class 1 b. Class 0 c. both Class 0 and Class 1 d. neither Class 0 nor Class 1
Added by Dennis N.
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To classify Observation 7 using k-NN with k = 4, we need to follow these steps: Show more…
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The scatter chart below displays 6 observations of a training set plotted according to the values of 2 quantitative variables (x1 and x2 expressed in the same units) that characterize the 6 observations. (The normalization of these values is not needed.) Three observations (#1, #2, #3) belong to Class 1, and the other three (#4, #5, #6) belong to Class 0. The chart also displays a new observation (#7) that must be classified using the k-NN method with the cutoff value of 0.5. Refer to Exhibit 9-2. For k = 1, the Euclidean distance of the k-nearest neighbor to Observation 7 is: A. 1 B. square root of 2 C. square root of 3 D. 2 QUESTION 12: Refer to Exhibit 9-2. For k = 1, Observation 7 should be classified in: A. Class 0 B. neither Class 1 nor Class 0 C. Class 1 D. both Class 1 and Class 0 QUESTION 13: Refer to Exhibit 9-2. For k = 2, Observation 7 should be classified in: A. Class 1 B. neither Class 1 nor Class 0 C. Class 0 D. both Class 1 and Class 0 QUESTION 14: Refer to Exhibit 9-2. For k = 3, Observation 7 should be classified in: A. Class 0 B. Class 1 C. neither Class 1 nor Class 0 D. both Class 1 and Class 0
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Suppose you want to predict the class of a new data point x=1 and y=1 using Euclidean distance in 3-NN: In which class does this data point belong to? A) + Class B) - Class C) Can't say D) None of these In the previous question, you now want to use 7-NN instead of 3-KNN. Which of the following x=1 and y=1 will belong to? A) + Class B) - Class C) Can't say
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