---
title: "EllipticEnvelope Anomaly Detection in JavaScript with @kanaries/ml"
description: "Detect multivariate outliers with robust covariance using the EllipticEnvelope JavaScript and TypeScript implementation in @kanaries/ml for browser and Node.js."
canonical_url: "https://ml.kanaries.net/docs/apis/covariance/ellipticEnvelope"
markdown_url: "https://ml.kanaries.net/docs/apis/covariance/ellipticEnvelope.md"
---
# EllipticEnvelope anomaly detection in JavaScript

## Algorithm overview

EllipticEnvelope fits a robust Gaussian-shaped envelope around inliers and flags samples whose robust Mahalanobis distance exceeds a contamination-based threshold. It works best when the inlier cloud is approximately elliptical.

## JavaScript implementation

`Covariance.EllipticEnvelope` wraps `MinCovDet`, so JavaScript applications can score or label tabular anomalies locally in a browser or Node.js process.

## Quick start example

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

const X = [[0, 0], [.1, .2], [.2, .1], [-.1, 0], [8, 8]];
const detector = new Covariance.EllipticEnvelope({ contamination: .2, randomState: 42 });
detector.fit(X);
console.log(detector.predict(X)); // 1 for inlier, -1 for outlier
```

## Detailed API reference

The constructor accepts `contamination` in `(0, 0.5]` plus all `MinCovDet` options. Methods are `fit(X)`, `scoreSamples(X)`, `decisionFunction(X)`, and `predict(X)`. The fitted `offset`, `location`, and `covariance` are readable properties.