1. What is special about the kNN algorithm? 2. What are the advantages and disadvantages of kNN as compared to ANN and SVM? 3. What are the critical success factors for a kNN implementation? 4. What is a similarity (or distance) measure? How can it be applied to both numerical and nominal valued variables? 5. What are the common applications of kNN?
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(a) (2 marks) In practice, what is a viable range for k in the k-Nearest Neighbours model? (b) (3 marks) Suppose that you have an extremely noisy data set. Would k-Nearest Neighbours with k = 1 work well on this data set? Briefly explain why, or why not. (c) Suppose you have given the following data, having two classes, where "+" represent a positive class and "-" is represent negative class. (i) (2 marks) Which of the following value of k in k-NN would minimize the leave one out cross validation accuracy (LOOCV)? (no explanation required) k = 3 k = 5 Either k = 3 or k = 5. Both have same same leave one out cross validation accuracy. (ii) (2 marks) What would be the leave on out cross validation accuracy for k = 5? (ie. proportion of the observations correctly predicted using LOOCV)
Sri K.
Problem 3. Consider a k-nearest neighbor classifier that uses the Euclidean distance metric on a binary classification task: How will the point (8,1) be classified using 1-nn? Explain. How will the point (8,1) be classified using 3-nn? Explain. How will the point (8,1) be classified using 4-nn? Explain.
Jon S.
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