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A KNN classifier assigns a test instance the majority class associated with its k nearest training instances. Distance between instances is measured using Euclidean distance. Suppose we have the following training set of positive (+) and negative (-) instances and a single test instance (O). (see short answer document) All instances are projected onto a vector space of two real-valued features (X and Y). Answer the following questions. Assume "unweighted" KNN (every nearest neighbor contributes equally to the final vote). (a) What would be the class assigned to this test instance for k=1 (6 points) (b) What would be the class assigned to this test instance for k=3 (6 points) (3) What would be the class assigned to this test instance for K=5 (6 points) (4) Setting K to a large value seems like a good idea. We get more votes! Given this particular training set, would you recommend setting K = 11? Why or why not? (7 points)