KNN Regressor#
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.
Interfaces: Estimator, Learner, Online, Parallel, Persistable
Data Type Compatibility: Depends on distance kernel
Parameters#
| # | Name | Default | Type | Description |
|---|---|---|---|---|
| 1 | k | 5 | int | The number of nearest neighbors to consider when making a prediction. |
| 2 | weighted | false | 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. |
Example#
use Rubix\ML\Regressors\KNNRegressor;
use Rubix\ML\Kernels\Distance\SafeEuclidean;
$estimator = new KNNRegressor(5, false, new SafeEuclidean());
Parallel#
This estimator implements the Parallel interface and can utilize a parallel processing backend such as Swoole to speed up inference:
use Rubix\ML\Backends\Swoole;
$estimator->setBackend(new Swoole(16));
Additional Methods#
This estimator does not have any additional methods.