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Using only the closest example (1NN) to determine the class is subject to errors due to:
A single atypical example.
Noise (i.e., an error) in the category label of a single training example.
More robust alternative is to find the k most-similar examples and return the majority category of these k examples.
Value of k is typically odd to avoid ties; 3 and 5 are most common.
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