The k-nearest neighbors algorithm classifies or predicts a value for a new data point by finding the k training examples closest to it under a chosen distance measure, such as Euclidean distance, and taking a majority vote of their class labels for classification or an average of their values for regression. It is a non-parametric, instance-based method that stores the entire training set and performs its computation at prediction time rather than fitting a model in advance. First described in the statistics literature in the early 1950s and formalized further by Thomas Cover and Peter Hart in a 1967 paper, it remains a standard, simple baseline in machine learning.
Facts
Time Complexity
Time Complexity (category) Credited ToEvelyn Fix and Joseph Hodges (1951), expanded by Thomas Cover 2 Connections
In Field
Source Wikipedia: K-nearest neighbors algorithm
Sources
1. K-Nearest Neighbors Algorithm (Wikipedia)
Wikipedia infobox: time complexity linearQuote, Wikipedia infobox: time complexity linear
linear
2. Wikipedia: K-nearest neighbors algorithm
Lead section, history
It was first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover.
In Group: Supervised and Online Learning Algorithms, Introduction
In statistics and machine learning, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method that assigns weightage only to the k (number of) nearest neighbors of an entity in making a decision about the entity.
In Field: Machine Learning, Lead sentence
k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method that assigns weightage only to the k (number of) nearest neighbors of an entity in making a decision about the entity.
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