K Nearest Neighbors#

A distance-based learning algorithm that locates the k nearest samples from the training set and predicts the class label that is most common.

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.

Interfaces: Estimator, Learner, Online, Probabilistic, Persistable

Data Type Compatibility: Depends on distance kernel


# 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.


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.