---
title: "Local Outlier Factor in JavaScript with @kanaries/ml"
description: "Detect density-based local anomalies in JavaScript or TypeScript with the LocalOutlierFactor implementation in @kanaries/ml for browser and Node.js."
canonical_url: "https://ml.kanaries.net/docs/apis/neighbors/localOutlierFactor"
markdown_url: "https://ml.kanaries.net/docs/apis/neighbors/localOutlierFactor.md"
---
# Local Outlier Factor in JavaScript

## Algorithm overview

Local Outlier Factor (LOF) compares a sample's local reachability density with the densities of its nearest neighbors. It is useful when anomalies live in sparse local regions even though the dataset has several clusters with different densities.

## JavaScript implementation

`Neighbors.LocalOutlierFactor` runs density-based anomaly detection in browser or Node.js code. Training-set detection and novelty detection are deliberately separate, matching sklearn's two usage modes.

## Quick start example

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

const X = [[0, 0], [.1, .2], [.2, .1], [1, 1], [8, 8]];
const labels = new Neighbors.LocalOutlierFactor({ nNeighbors: 2, contamination: .2 })
  .fitPredict(X);

const novelty = new Neighbors.LocalOutlierFactor({ nNeighbors: 2, novelty: true });
novelty.fit(X);
console.log(novelty.predict([[.15, .1], [20, 20]]));
```

## Detailed API reference

The constructor accepts `nNeighbors?: number`, `contamination?: number | 'auto'`, and `novelty?: boolean`. With `novelty: false`, call `fitPredict(X)` and read `negativeOutlierFactor`. With `novelty: true`, call `fit(X)` followed by `scoreSamples(X)`, `decisionFunction(X)`, or `predict(X)`. The fitted threshold is available as `offset`.