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In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Nearest neighbor methods are a popular class of nonparametric estimators with several<lb>desirable properties, such as adaptivity to different distance scales in different regions of<lb>space. Prior work on convergence rates for nearest neighbor classification has not fully<lb>reflected these subtle properties. We analyze the behavior of these estimators in metric<lb>spaces and provide finite-sample, distribution-dependent rates of convergence under min-<lb>imal assumptions. As a by-product, we are able to establish the universal consistency of<lb>nearest neighbor in a broader range of data spaces than was previously known. We illus-<lb>trate our upper and lower bounds by introducing smoothness classes that are customized<lb>for nearest neighbor classification.
Output:
Rates of Convergence for Nearest Neighbor Classification