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KNN Regressor#

A version of the K Nearest Neighbors algorithm that uses the average (mean) outcome of the k nearest data points to an unknown sample to make continuous-valued predictions suitable for regression problems.

Note: KNN is considered a lazy learner because it does the majority of its computation during inference. For a faster spatial tree-accelerated version, see KD Neighbors Regressor.

Interfaces: Estimator, Learner, Online, Persistable

Data Type Compatibility: Depends on distance kernel

Parameters#

# Param Default Type Description
1 k 5 int The number of nearest neighbors to consider when making a prediction.
2 weighted true bool Should we consider the distances of our nearest neighbors when making predictions?
3 kernel Euclidean Distance The distance kernel used to compute the distance between sample points.

Example#

use Rubix\ML\Regressors\KNNRegressor;
use Rubix\ML\Kernels\Distance\SafeEuclidean;

$estimator = new KNNRegressor(2, false, new SafeEuclidean());

Additional Methods#

This estimator does not have any additional methods.