In
pattern recognition, the
k-Nearest Neighbors algorithm (or
k-NN for short) is a
non-parametric method used for
classification and
regression. In both cases, the input consists of the
k closest training examples in the
feature space. The output depends on whether
k-NN is used for classification or regression:
- In k-NN classification, the output is a class 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.
- In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.