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k Nearest Neighbor

  • 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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