Radius Neighbors is a spatial tree-based classifier that takes the weighted vote of each neighbor within a fixed user-defined radius measured by a kernelized distance function. Since the radius of the search can be constrained, Radius Neighbors is more robust to outliers than K Nearest Neighbors.
Note: Unknown samples with no training samples within radius are labeled -1. As such, Radius Neighbors is also a quasi anomaly detector.
Data Type Compatibility: Continuous
|1||radius||1.0||float||The radius within which points are considered neighboors.|
|2||weighted||true||bool||Should we use the inverse distances as confidence scores when making predictions?|
|3||tree||BallTree||object||The spatial tree used to run range searches.|
Return the base spatial tree instance:
public tree() : Spatial
use Rubix\ML\Classifiers\RadiusNeighbors; use Rubix\ML\Graph\Trees\KDTree; use Rubix\ML\Kernels\Distance\Manhattan; $estimator = new RadiusNeighbors(50.0, false, new KDTree(100, new Manhattan()));