6. Consider a binary classification problem using 1-nearest neighbors with the Euclidean distance metric. We have N 1-dimensional training points x(1), x(2), . . . x(N ) and corresponding labelsy(1), y(2), . . . y(N ) with x(i ) ∈ R and y(i ) ∈ {0, 1}. Assume the points x(1), x(2), . . . x(N ) are in ascending order by value. If there are ties during the 1-NN algorithm, we break ties by choosing the labelcorresponding to the x(i ) with lower value. 6.Consider a binary classification problem using l-nearest neighbors with the Euclidean dis-tance metric. We have N 1-dimensional training points x", x".... and corresponding labelsare in ascend-ing order by vahue. If there are ties during the 1-NN algorithm, we break ties by choosing the labely". P..M with x ER and y" e (0. 1. Assume the points x. x.coresponding to the x" with lower value.Is it possible to build a decision tree that behaves exactly the same as the1-nearest neighbor classifier? Assume that the decision at each node takes the fom of" x st orx >1," wheret ER.(a)YesO NoIf your answer is yes, please explain how you will construct the decision tree. If your answer is no,explain why it's not possible.Your answer.Decision Trees, k-NN, RegressionNow assume the training points are 2-dimensionalwhere x" = . ) ER? and the decision at each node takes the form of “ xy st or x, > t,"where t eR andj e (1. 2. Give an example with at most 3 training points for which it isn'tpossible to build a decision tree that behaves exactly the same as a l-nearest neighbor classifier.Let's add a dimension!Your answer:= . ) ER* and(c)the decision at each node takes the form of "x, st or x, > 1," wheret ER and j e 1, 2, underwhat conditions is it possible to buzild a decision tree that behaves exactly the same as a l-nearestneighbor classifier? Explain why it is possible to build the decision tree as stated in part (a) but, ingeneral, not possible in part (6).Assuming we have 2-dimensional training points

a) In the kNN algorithm, the training time is the same for any value of k. As k is increased, a…

Answered: Consider a binary classification…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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6. Consider a binary classification problem using 1-nearest neighbors with the Euclidean distance metric. We have N 1-dimensional training points x(1), x(2), . . . x(N ) and corresponding labels
y(1), y(2), . . . y(N ) with x(i ) ∈ R and y(i ) ∈ {0, 1}. Assume the points x(1), x(2), . . . x(N ) are in ascending order by value. If there are ties during the 1-NN algorithm, we break ties by choosing the label
corresponding to the x(i ) with lower value.