Neighbors.KNearstNeighbors

constructor(
    kNeighbors: number = 5,
    weightType: IWeightType = 'uniform',
    distanceType: Distance.IDistanceType = 'euclidiean',
    pNorm: number = 2
)

Parameters

• kNeighbors (number): number of neighbors used for prediction. Default is 5.
• weightType ('uniform' | 'distance'): weighting strategy for voting. 'uniform' counts every neighbor equally while 'distance' weighs closer samples more.
• distanceType (Distance.IDistanceType): distance metric. Defaults to 'euclidiean' but other metrics from Distance can be used.
• pNorm (number): order of the norm when using Minkowski distance. Default is 2.

Algorithm

KNN is a lazy classifier. During prediction it computes the distance between the query sample and all training points. The closest kNeighbors points vote for the label. Voting can be uniform or weighted by inverse distance depending on weightType.

Example

const knn = new KNearstNeighbors(3, 'distance', '2-norm');
knn.fit(trainX, trainY);
const result = knn.predict(trainX);

Practical guide: KNearstNeighbors in JavaScript and TypeScript

KNearstNeighbors predicts from nearby examples and works as a strong non-parametric baseline for classification and regression.

When to use KNearstNeighbors

• Decision boundaries are irregular and hard to model parametrically.
• You need straightforward behavior that is easy to reason about.
• Training time should be minimal and inference latency is acceptable.

Implementation workflow

1. Scale numeric features and choose a distance metric.
2. Fit on labeled examples and test multiple k values.
3. Validate accuracy/latency tradeoffs before deployment.

JavaScript deployment notes

• Prefer feature scaling for distance-based and gradient-based algorithms to improve stability.
• In browser apps, run heavy training in Web Workers to keep UI interactions smooth.
• Keep a simple baseline from the same module as a fallback model for comparison.

Search intents this page targets

• KNearstNeighbors JavaScript
• KNearstNeighbors TypeScript
• KNearstNeighbors browser machine learning
• @kanaries/ml KNearstNeighbors