K Nearest Neighbors#
A brute-force distance-based learning algorithm that locates the k nearest samples from the training set and predicts the class label that is most common. 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.
Interfaces: Estimator, Learner, Online, Probabilistic, 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\Classifiers\KNearestNeighbors;
use Rubix\ML\Kernels\Distance\Manhattan;
$estimator = new KNearestNeighbors(3, false, new Manhattan());
Additional Methods#
This estimator does not have any additional methods.
Last update:
2021-03-27