!!! Overview [1]
[{$pagename}] ([KNN]) is a [Supervised Learning], non-parametric method used in [Machine Learning] for [classification] and [regression]


[{$pagename}] in both cases, the input consists of the k closest [Training dataset] in the [feature] space. The output depends on whether k-NN is used for classification or regression:

* [{$pagename}] [classification], the output is a [Classification] membership. An object is classified by a __majority vote of its neighbors__, with the object being assigned to the class most common among its k nearest neighbors (k is a positive [integer], typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.
* [{$pagename}] [regression], the output is the property value for the object. This value is the __average of the values __of its k nearest neighbors.
[{$pagename}] is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until [classification]. The [{$pagename}] [algorithm] is among the simplest of all [Machine Learning Algorithms].

Both for [classification] and [regression], a useful technique can be to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.

The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.

A peculiarity of the [{$pagename}] [algorithm] is that it is sensitive to the local structure of the [data]. 

!! More Information
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* [#1] - [K-nearest_neighbors_algorithm|Wikipedia:K-nearest_neighbors_algorithm|target='_blank'] - based on information obtained 2018-04-11-