---
title: "Kernel Ridge Regression and Kernel Density in JavaScript"
description: "Use nonlinear KernelRidge prediction and KernelDensity estimation in JavaScript or TypeScript with @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/kernel"
markdown_url: "https://ml.kanaries.net/docs/apis/kernel/index.html.md"
---
# Kernel methods in JavaScript

## Algorithm overview

Kernel methods compare samples through a similarity function instead of explicitly expanding nonlinear features. `KernelRidge` solves regularized regression in the dual; `KernelDensity` estimates a probability density from nearby observations.

## JavaScript implementation

`@kanaries/ml` keeps both estimators in the JavaScript runtime: fit nonlinear regression in a Node.js service, or score small density models directly in an interactive browser workflow without a Python inference endpoint. Choose this implementation when model state and product code need to share the same typed API; benchmark larger training sets because kernel matrices and pairwise scoring grow with the number of samples.

## Interactive Kernel Ridge regression playground

Tune the RBF bandwidth through `gamma`, switch datasets, and add your own observations. The nonlinear curve is fitted live with `Kernel.KernelRidge`.

> The HTML version includes an interactive Kernel 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/kernel).

## Quick start

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

const X = [[0], [1], [2]];
const regressor = new Kernel.KernelRidge({ kernel: 'rbf', gamma: 0.5 });
regressor.fit(X, [0, 1, 4]);
const density = new Kernel.KernelDensity({ bandwidth: 0.8 });
density.fit(X);
console.log({ prediction: regressor.predict([[1.5]]), logDensity: density.scoreSamples([[1.5]]) });
```

## Detailed API reference

KernelRidge supports linear, polynomial, RBF, sigmoid, and cosine kernels. KernelDensity supports Gaussian, tophat, Epanechnikov, exponential, linear, and cosine radial kernels and exposes `scoreSamples` and `score`.