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The Nearest Neighbor Algorithm - Classes

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<strong>The</strong> Curse of Noisy/Irrelevant Features<br />

• NN also breaks down when data contains irrelevant/noisy features.<br />

• Consider a 1-d problem where query x is at the origin, our nearest<br />

neighbor is x 1 at 0.1, and our second nearest neighbor is x 2 at 0.5.<br />

• Now add a uniformly random noisy feature.<br />

– P(||x 2 ’ - x||

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