---
title: "ElasticNet in JavaScript with @kanaries/ml"
description: "Fit linear regression with combined L1 and L2 regularization using the ElasticNet JavaScript and TypeScript implementation in @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/linear/elasticNet"
markdown_url: "https://ml.kanaries.net/docs/apis/linear/elasticNet.md"
---
# ElasticNet in JavaScript

## Algorithm overview

ElasticNet combines L1 and L2 penalties. It is useful when you want sparse-ish linear models but pure Lasso is too unstable, especially with correlated features.

## JavaScript implementation

`@kanaries/ml` provides `Linear.ElasticNet` for browser and Node.js regression workflows. `l1Ratio` controls the balance between Ridge-like and Lasso-like behavior.

## Interactive ElasticNet playground

Tune the regularization strength and experiment with noisy or contaminated data. The displayed prediction is fitted live with `Linear.ElasticNet` and updates when you add a point.

> The HTML version includes an interactive ElasticNet playground powered by @kanaries/ml. You can change the dataset, noise, and model controls; add observations; and inspect live predictions plus train and holdout metrics. The runnable guide and API reference continue below. [Open the HTML page](https://ml.kanaries.net/docs/apis/linear/elasticNet).

## Quick start example

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

const X = [[0, 1], [1, 1], [2, 0], [3, 0]];
const y = [1, 2, 3, 4];

const model = new Linear.ElasticNet({ alpha: 0.1, l1Ratio: 0.5 });
model.fit(X, y);
const pred = model.predict([[4, 0]]);
console.log(pred);
```

## Detailed API reference

```ts
new Linear.ElasticNet(props?: {
  alpha?: number;
  l1Ratio?: number;
  fitIntercept?: boolean;
  maxIter?: number;
  tol?: number;
})
```

Options:

- `alpha?: number`, default `1`.
- `l1Ratio?: number`, default `0.5`. Must be between `0` and `1`.
- `fitIntercept?: boolean`, default `true`.
- `maxIter?: number`, default `1000`.
- `tol?: number`, default `1e-6`.

Methods:

- `fit(X: number[][], Y: number[]): void`
- `predict(X: number[][]): number[]`

When `l1Ratio` is `0`, the implementation delegates to Ridge regression. When `l1Ratio` is `1`, it delegates to Lasso regression.