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K Nearest Neighbors#

A brute-force distance-based learning algorithm that locates the k nearest samples from the training set and predicts the class label that is most common. K Nearest Neighbors (KNN) is considered a lazy learner because it performs most of its computation at inference time.

Note: For a faster spatial tree-accelerated version of KNN, see KD Neighbors.

Interfaces: Estimator, Learner, Online, Probabilistic, 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\Classifiers\KNearestNeighbors;
use Rubix\ML\Kernels\Distance\Manhattan;

$estimator = new KNearestNeighbors(3, true, new Manhattan());

Additional Methods#

This estimator does not have any additional methods.