In k-NN algorithm, given a set of training examples and the value of k < size of training set (n), the algorithm predicts the class of a test example to be the
A) Most frequent class among the classes of k closest training examples.
B) Least frequent class among the classes of k closest training examples.
C) Class of the closest point.
D) Most frequent class among the classes of the k farthest training examples.
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A. Most frequent class among the classes of k closest training examples.
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