[source] # 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](k-nearest-neighbors.md) is that it is much more efficient, however it cannot be partially trained. **Interfaces:** [Estimator](../estimator.md), [Learner](../learner.md), [Probabilistic](../probabilistic.md), [Persistable](../persistable.md) **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 ```php 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: ```php public tree() : Spatial ```