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k-Nearest Neighbors Prediction

k-Nearest Neighbors Prediction

k-Nearest Neighbors Prediction:

The k-nearest neighbors (k-NN) prediction is a method to predict a value of a target variable in a given record, using as a reference point a training set of similar objects. The basic idea is to choose k objects from the training set that are closest to the given object in terms of the predictor variables, then to form the weighted average of target variable for those k objects. The weights are usually chosen inversely proportionally to the distances from the target object.

See also: the chapter from XLMiner help, and the online short course Introduction to Data Mining.

Browse Other Glossary Entries

Courses Using This Term

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Cluster Analysis
This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. Methods discussed include hierarchical clustering, k-means clustering, two-step clustering, and normal mixture models for continuous variables.
Multivariate Statistics
This course will teach you key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis, and classification.
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