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K-d Neighbors#

A fast K Nearest Neighbors algorithm that uses a binary search tree (BST) to divide the training set into neighborhoods. K-d Neighbors then does a binary search to locate the nearest neighborhood of an unknown sample and prunes all neighborhoods whose bounding box is further than the k'th nearest neighbor found so far. The main advantage of K-d Neighbors over brute force KNN is that it is much more efficient, however it cannot be partially trained.

Interfaces: Estimator, Learner, 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 tree KDTree Spatial The spatial tree used to run nearest neighbor searches.

Additional Methods#

Return the base spatial tree instance:

public tree() : Spatial

Example#

use Rubix\ML\Classifiers\KDNeighbors;
use Rubix\ML\Graph\Trees\BallTree;
use Rubix\ML\Kernels\Distance\Minkowski;

$estimator = new KDNeighbors(3, false, new BallTree(40, new Minkowski()));