# Standard 1. HINT: Read Standard 2 before embarking on this. Coding will be easier in several…

1. Standard 1. HINT: Read Standard 2 before embarking on this. Coding will be easier in several cases. The following artificial dataset has 4 numeric features and a nominal class label (A, B, or C). 8.1,3.8,1.3,0.2,A
8.5,3.4,1.3,0.2,C

```4.5,3.1,1.6,0.2,A
8.0,4.6,1.4,0.1,B
8.9,3.3,4.0,1.3,C
6.8,2.5,4.6,1.8,A
8.5,2.8,4.4,1.3,B
6.3,3.3,4.5,1.4,C
4.5,2.4,3.2,1.0,A
5.5,3.6,6.5,2.2,B
5.5,2.6,6.8,2.3,C
6.0,2.2,8.0,1.4,A
6.9,3.2,8.5,9.3,B
8.6,2.2,4.6,2.0,C
```

Assignment 2 Page 2

Explain how and why kNN would classify the new example 8.0, 2.6, 4.6, 0.3 for k = 1, 4, and 6. Do this once using Euclidean distance and then again using Manhattan distance. If there is a tie, either for the ith neighbor or

between classes, state how you resolved the tie and why you did it that way.

To receive any credit, show the computation that underlies your answers in one of the following ways:

• Calculate by hand and submit typed (not handwritten) computations.

• Submit a spreadsheet.

• Write a program in a language of your choice, include your code, your output, and a ReadMe with running

instructions. If you take code from any repository you must cite it. (See the handout on plagiarism.)
To earn a +4: Look up and then repeat this problem with any one of the following metrics: Minkowski norms, Levenshtein and Hamming distances, Mahalanobis distance, Chebyshev. State your results and how they differ from the other two distance functions.

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