The Nearest Neighbor Algorithm - Classes

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Effect of K

K=1 K=15

Figures from Hastie, Tibshirani and Friedman (Elements of Statistical Learning)

Larger k produces smoother boundary effect and can reduce the

impact of class label noise.

But when K = N, we always predict the majority class

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Example: K-Nearest Neighbor K = 4 New example Find the k nearest neighbors and have them vote. Has a g smoothing effect. This is especially good when there is noise in the class labels.

Effect of K K=1 K=15 Figures from Hastie, Tibshirani and Friedman (Elements of Statistical Learning) Larger k produces smoother boundary effect and can reduce the impact of class label noise. But when K = N, we always predict the majority class

Page 1 and 2: The Nearest Neighbor Algorithm •
Page 3 and 4: Decision Boundaries: The Voronoi Di
Page 5: Decision Boundaries With large numb
Page 9 and 10: Distance Weighted Nearest Neighbor
Page 11 and 12: The Curse of Dimensionality: Illust
Page 13 and 14: Curse of Noise (2) Location of x 1
Page 15: Nearest Neighbor Summary • Advant

The Nearest Neighbor Algorithm - Classes

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