---
title: "RadiusNeighborsClassifier in JavaScript with @kanaries/ml"
description: "Classify samples from all neighbors inside a radius using the RadiusNeighborsClassifier JavaScript and TypeScript implementation in @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/neighbors/radiusNeighborsClassifier"
markdown_url: "https://ml.kanaries.net/docs/apis/neighbors/radiusNeighborsClassifier.md"
---
# RadiusNeighborsClassifier in JavaScript

## Algorithm overview

RadiusNeighborsClassifier assigns a class from all training samples within a fixed distance radius. It is useful when a fixed neighborhood size is more meaningful than a fixed number of neighbors.

## JavaScript implementation

`@kanaries/ml` exposes `Neighbors.RadiusNeighborsClassifier` with uniform or distance-weighted voting in browser and Node.js applications.

## Quick start example

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

const clf = new Neighbors.RadiusNeighborsClassifier({
  radius: 1.5,
  weights: 'uniform',
  outlierLabel: -1,
});

clf.fit([[0], [1], [4]], [0, 0, 1]);
const pred = clf.predict([[0.5], [10]]);
console.log(pred);
```

## Detailed API reference

```ts
new Neighbors.RadiusNeighborsClassifier(props?: {
  radius?: number;
  weights?: 'uniform' | 'distance';
  metric?: Distance.IDistanceType;
  p?: number;
  outlierLabel?: number | null;
})
```

Methods:

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

If no neighbors are found, `predict` returns `outlierLabel` when it is not `null`; otherwise it throws.