K-d Neighbors#
A fast K Nearest Neighbors algorithm that uses a binary search tree (BST) to divide the training set into neighborhoods that contain samples that are close together spatially. 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#
| # | Name | Default | Type | Description |
|---|---|---|---|---|
| 1 | k | 5 | int | The number of nearest neighbors to consider when making a prediction. |
| 2 | weighted | false | 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. |
Example#
use Rubix\ML\Classifiers\KDNeighbors;
use Rubix\ML\Graph\Trees\BallTree;
use Rubix\ML\Kernels\Distance\Minkowski;
$estimator = new KDNeighbors(10, false, new BallTree(40, new Minkowski()));
Additional Methods#
Return the base spatial tree instance:
public tree() : Spatial