KNN Regressor#
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.
Interfaces: Estimator, Learner, Online, Persistable
Data Type Compatibility: Continuous
Parameters#
| # | Param | Default | Type | Description |
|---|---|---|---|---|
| 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. |
Additional Methods#
This estimator does not have any additional methods.
Example#
use Rubix\ML\Regressors\KNNRegressor;
use Rubix\ML\Kernels\Distance\Minkowski;
$estimator = new KNNRegressor(2, false, new Minkowski(3.0));