The Nearest Neighbor Algorithm - Classes

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Question: how to choose k? • Can we choose k to minimize the mistakes that we make on training examples (training error)? K=20 K=1 A model selection problem that we will Model complexity study later
Distance Weighted <strong>Nearest</strong> <strong>Neighbor</strong> • It makes sense to weight the contribution of each example according to the distance to the new query example – Weight varies inversely with the distance, such that examples closer to the query points get higher weight • Instead of only k examples, we could allow all training examples to contribute – Shepard’s method (Shepard 1968)
Page 1 and 2: The Nearest Neighbor Algorithm •
Page 3 and 4: Decision Boundaries: The Voronoi Di
Page 5 and 6: Decision Boundaries With large numb
Page 7: Effect of K K=1 K=15 Figures from H
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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