---
title: "RidgeClassifier in JavaScript with @kanaries/ml"
description: "Train L2-regularized linear classifiers with the RidgeClassifier JavaScript and TypeScript implementation in @kanaries/ml for browser and Node.js applications."
canonical_url: "https://ml.kanaries.net/docs/apis/linear/ridgeClassifier"
markdown_url: "https://ml.kanaries.net/docs/apis/linear/ridgeClassifier.md"
---
# RidgeClassifier in JavaScript

## Algorithm overview

RidgeClassifier adapts ridge regression for classification by fitting one-vs-rest linear models and selecting the class with the highest score. It is useful for fast, interpretable classification on numeric tabular data.

## JavaScript implementation

`@kanaries/ml` exposes `Linear.RidgeClassifier` with a JavaScript estimator API. It supports binary and multiclass numeric labels through one-vs-rest fitting.

## Quick start example

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

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

const clf = new Linear.RidgeClassifier({ alpha: 1 });
clf.fit(X, y);
const pred = clf.predict([[1, 1], [3, 3]]);
console.log(pred);
```

## Detailed API reference

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

Methods:

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

The classifier sorts numeric classes ascending and fits one `RidgeRegression` model per class.