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);