K Nearest Neighbors (KNN) is a brute-force distance-based learner that locates the k nearest training samples from the training set and averages their labels to make a prediction. K Nearest Neighbors (KNN) is considered a lazy learner because it performs most of its computation at inference time.
Note: For a faster spatial tree-accelerated version of KNN, see KD Neighbors Regressor.
Data Type Compatibility: Depends on distance kernel
|1||k||5||int||The number of nearest neighbors to consider when making a prediction.|
|2||weighted||true||bool||Should we consider the distances of our nearest neighbors when making predictions?|
|3||kernel||Euclidean||Distance||The distance kernel used to compute the distance between sample points.|
use Rubix\ML\Regressors\KNNRegressor; use Rubix\ML\Kernels\Distance\SafeEuclidean; $estimator = new KNNRegressor(2, false, new SafeEuclidean());
This estimator does not have any additional methods.