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This topic has appeared in the trending rankings 1 time(s) in the past year. While it does not trend frequently, its appearance suggests a renewed or concentrated surge of public interest.
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K-nearest_neighbors_algorithm entered the ranking for the first time today at position #. This is its highest position ever recorded.
This topic has appeared in the English Wikipedia rankings 1 time. It first appeared on 2026-05-14 and was most recently seen on 2026-05-14.
In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. In classification, a new example is assigned a label based on the labels of its k nearest training examples; in regression, the prediction is computed from the values of those neighbors.
Most often, it is used for classification, as a k-NN classifier, the output of which is a class membership. An object is classified by a plurality 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.
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