K Nearest Neighbors#
A distance-based learning algorithm that locates the k nearest samples from the training set and predicts the class label that is most common. A kernelized distance function allows the user to specify to the learner a definition of distance.
Note: This learner is considered a lazy learner because it does the majority of its computation during inference. For a fast spatial tree-accelerated version, see KD Neighbors.
Data Type Compatibility: Depends on the distance kernel
|1||k||5||int||The number of nearest neighbors to consider when making a prediction.|
|2||weighted||true||bool||Should we use the inverse distances as confidence scores when making predictions?|
|3||kernel||Euclidean||object||The distance kernel used to compute the distance between sample points.|
This estimator does not have any additional methods.
use Rubix\ML\Classifiers\KNearestNeighbors; use Rubix\ML\Kernels\Distance\Manhattan; $estimator = new KNearestNeighbors(3, true, new Manhattan());