Predictive and Specialized Modeling > K Nearest Neighbors > Overview of the K Nearest Neighbors Platform
Publication date: 08/13/2020

Overview of the K Nearest Neighbors Platform

The K Nearest Neighbors platform predicts a response value based on the responses of the k nearest neighbors. The k nearest neighbors to a given observation are determined by identifying the k smallest Euclidean distances between the predictor values for that observation and the predictor values for each of the other observations. The K Nearest Neighbors platform models both continuous and categorical responses.

A potential drawback of the k nearest neighbors method is that for large scale problems, the prediction formula is often complex and hard to interpret, limiting its usefulness. In addition, K Nearest Neighbors does not calculate probabilities for categorical responses. For more information about the k nearest neighbors method, see Hastie et al. (2009), Hand et al. (2001), and Shmueli et al. (2017).

Continuous Responses

For a continuous response, the predicted value is the average of the responses for the k nearest neighbors. Each continuous predictor is scaled by its standard deviation. With this scaling, a single predictor with a large range does not excessively influence the distance calculation. Missing values for a continuous predictor are replaced by the mean of that predictor. See Example of K Nearest Neighbors with Continuous Response.

Categorical Responses

For a categorical response, the predicted value is the most frequent response level for the k nearest neighbors. If two or more levels are tied as the most frequent levels, the predicted response is assigned by selecting one of these levels at random.

Note: Because ties for most frequent levels in the case of a categorical response are broken at random, results from independent runs of the platform might differ. To obtain reproducible results, use the Set Random Seed option in the launch window or include the Set Random Seed() function in a JSL script.

In the categorical prediction models, each categorical predictor is expressed in terms of indicator variables, with one indicator variable representing each level. A row with a missing value for a categorical predictor is represented by values of zero on all indicator variables for that predictor.

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