You may not use the knn or dist commands in R for this question. You may...
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You may not use the knn or dist commands in R for this question. You may perform calculations by hand (and attach to assignment) or manually in R (including commands and output in the document). Suppose we have the following observations: X1 X2 X3 Observation Y 1 Blue 0 2 0 2 Yellow -2 -1 0 3 Yellow -1 0 1 4 Blue 0 1 3 5 Yellow 1 1 1 6 Yellow 0 3 0 (a) Compute the Euclidean distance between all points and a new observation at the origin (0, 0, 0). (b) Perform K-nearest neighbours classification for this new observation with K=1. What are the resulting classification probabilities at the origin? How would KNN classify the origin point? (c) Perform K-nearest neighbours classification for this new observation with K=3. What are the resulting classification probabilities at the origin? How would KNN classify the origin point? You may not use the knn or dist commands in R for this question. You may perform calculations by hand (and attach to assignment) or manually in R (including commands and output in the document). Suppose we have the following observations: X1 X2 X3 Observation Y 1 Blue 0 2 0 2 Yellow -2 -1 0 3 Yellow -1 0 1 4 Blue 0 1 3 5 Yellow 1 1 1 6 Yellow 0 3 0 (a) Compute the Euclidean distance between all points and a new observation at the origin (0, 0, 0). (b) Perform K-nearest neighbours classification for this new observation with K=1. What are the resulting classification probabilities at the origin? How would KNN classify the origin point? (c) Perform K-nearest neighbours classification for this new observation with K=3. What are the resulting classification probabilities at the origin? How would KNN classify the origin point?
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Related Book For
Income Tax Fundamentals 2013
ISBN: 9781285586618
31st Edition
Authors: Gerald E. Whittenburg, Martha Altus Buller, Steven L Gill
Posted Date:
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