K-d Neighbors Regressor#
A fast implementation of KNN Regressor using a spatially-aware binary tree for nearest neighbors search. K-d Neighbors Regressor works by locating the neighborhood of a sample via binary search and then does a brute force search only on the samples close to or within the neighborhood of the unknown sample. The main advantage of K-d Neighbors over brute force KNN is inference speed, however, it cannot be partially trained.
Interfaces: Estimator, Learner, 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\Regressors\KDNeighborsRegressor;
use Rubix\ML\Graph\Trees\BallTree;
$estimator = new KDNeighborsRegressor(20, true, new BallTree(50));
Additional Methods#
Return the base spatial tree instance:
public tree() : Spatial