---
title: "Ridge Regression in JavaScript with @kanaries/ml"
description: "Use the RidgeRegression JavaScript and TypeScript implementation in @kanaries/ml for regularized linear regression in browser and Node.js applications."
canonical_url: "https://ml.kanaries.net/docs/apis/linear/ridgeRegression"
markdown_url: "https://ml.kanaries.net/docs/apis/linear/ridgeRegression.md"
---
# Ridge Regression in JavaScript

## Algorithm overview

Ridge regression is linear regression with L2 regularization. It is useful when features are correlated, coefficients are unstable, or an ordinary least squares model overfits.

## JavaScript implementation

`@kanaries/ml` exposes `Linear.RidgeRegression` for JavaScript and TypeScript projects. It follows the familiar `fit` and `predict` estimator shape and works with numeric arrays in browser or Node.js.

## Interactive Ridge regression playground

Tune the L2 penalty and data noise below. `Linear.RidgeRegression` refits in the browser after every change, including observations you add directly to the chart.

> The HTML version includes an interactive Ridge Regression 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/ridgeRegression).

## Quick start example

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

const X = [[0], [1], [2], [3]];
const y = [1, 3, 5, 7];

const model = new Linear.RidgeRegression({ alpha: 0.5, fitIntercept: true });
model.fit(X, y);
const pred = model.predict([[4]]);
console.log(pred);
```

## Detailed API reference

```ts
new Linear.RidgeRegression(props?: {
  alpha?: number;
  fitIntercept?: boolean;
})
```

Options:

- `alpha?: number`, default `1`. L2 penalty strength. Must be finite and greater than or equal to 0.
- `fitIntercept?: boolean`, default `true`.

Methods:

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

`predict` throws if the model has not been fitted or if the feature count differs from the fitted data.

## sklearn-style alias: `Ridge`

`Linear.Ridge` extends `Linear.RidgeRegression` with identical options and methods. Use it when you prefer the scikit-learn class name:

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

const model = new Linear.Ridge({ alpha: 0.5, fitIntercept: true });
model.fit([[0], [1], [2], [3]], [1, 2, 3, 4]);
const pred = model.predict([[4]]);
```