4. Let the table with the following training data be given. Num. X1 X2 Class 1...

 

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4. Let the table with the following training data be given. Num. X1 X2 Class 1 6 1 0 2 7 3 0 3 8 2 0 4 st 9 0 0 5 8 4 1 6 8 6 1 7 9 2 1 8 9 5 1 In the following, use the Manhattan distance d(a, b), defined d(a, b) = |a- b| + laz-bal, to determine the distance d between two data points a = (a1, a2) and b = (b1, bz). 1) Classify the vector v = (8, 3.5) with the nearest neighbor method. (10 Points) 2) Classify the vector = (8, 3.5) with the k nearest neighbor method for k = 2, 3, 5 (10 Points) 4. Let the table with the following training data be given. Num. X1 X2 Class 1 6 1 0 2 7 3 0 3 8 2 0 4 st 9 0 0 5 8 4 1 6 8 6 1 7 9 2 1 8 9 5 1 In the following, use the Manhattan distance d(a, b), defined d(a, b) = |a- b| + laz-bal, to determine the distance d between two data points a = (a1, a2) and b = (b1, bz). 1) Classify the vector v = (8, 3.5) with the nearest neighbor method. (10 Points) 2) Classify the vector = (8, 3.5) with the k nearest neighbor method for k = 2, 3, 5 (10 Points)

Answer rating: 100% (QA)

To categorize the vector 835 v835 requires us to find the Manhattan distances between v and each tra... View the full answer