---
title: "NearestCentroid in JavaScript with @kanaries/ml"
description: "Classify samples by the closest class centroid using the NearestCentroid JavaScript and TypeScript implementation in @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/neighbors/nearestCentroid"
markdown_url: "https://ml.kanaries.net/docs/apis/neighbors/nearestCentroid.md"
---
# NearestCentroid in JavaScript

## Algorithm overview

NearestCentroid represents each class by the mean feature vector of its training samples, then predicts the class with the closest centroid. It is a fast, interpretable baseline for numeric classification.

## JavaScript implementation

`@kanaries/ml` exposes `Neighbors.NearestCentroid` for browser and Node.js applications. It supports the same distance type names used by `Metrics.Distance`.

## Quick start example

```ts
import { Neighbors } from '@kanaries/ml';

const clf = new Neighbors.NearestCentroid({ metric: 'euclidean' });
clf.fit([[0, 0], [0, 1], [4, 4], [5, 4]], [0, 0, 1, 1]);
const pred = clf.predict([[1, 1], [4, 5]]);
console.log(pred);
```

## Detailed API reference

```ts
new Neighbors.NearestCentroid(props?: {
  metric?: Distance.IDistanceType;
  p?: number;
})
```

Methods:

- `fit(trainX: number[][], trainY: number[]): void`
- `predict(testX: number[][]): number[]`

Class labels are numeric. Ties are resolved by the smaller class label.