k-Nearest Neighbor Prediction Functions

• Published in: Algorithms for Data Science pp. 279 - 312
• Main Authors: Steele, Brian, Chandler, John, Reddy, Swarna
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2016
• Subjects: Confusion Matrice; Confusion Matrix; Neighborhood Size; Pattern Vector; Prediction Function
• ISBN: 3319457950; 9783319457956
• DOI: 10.1007/978-3-319-45797-0_9

The purpose of the k-nearest neighbor prediction function is to predict a target variable from a predictor vector. Commonly, the target is a categorical variable, a label identifying the group from which the observation was drawn. The analyst has no knowledge of the membership label but does have the information coded in the attributes of the predictor vector. The predictor vector and the k-nearest neighbor prediction function generate a prediction of membership. In addition to qualitative attributes, the k-nearest neighbor prediction function may be used to predict quantitative target variables. The k-nearest-neighbor prediction functions are conceptually and computationally simple and often rival far more sophisticated prediction functions with respect to accuracy. The functions are nonparametric in the sense that the mathematical basis supporting the prediction functions is not a model. Instead the k-nearest neighbor prediction function utilizes a set of training observations on target and predictor vector pairs and, in essence examines the target values of the training observations nearest to the target. If the target variable is a group membership label, the target is predicted to be to the most common label among the nearest neighbors. If the target is quantitative, then the prediction is an average of the target values associated with the nearest neighbors.