A version of the K Nearest Neighbors algorithm that uses the average (mean) outcome of the k nearest data points to make continuous valued predictions suitable for regression problems.
Note: This learner is considered a lazy learner because it does the majority of its computation during inference. For a fast spatial tree-based version, see KD Neighbors Regressor.
Data Type Compatibility: Continuous
|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\Regressors\KNNRegressor; use Rubix\ML\Kernels\Distance\Minkowski; $estimator = new KNNRegressor(2, false, new Minkowski(3.0));