# ml-regression-polynomial

> Polynomial Regression

Latest version **4.0.0** (published 2026-04-18) · MIT license · 0 weekly downloads

## Install

```sh
npm install ml-regression-polynomial
pnpm add ml-regression-polynomial
yarn add ml-regression-polynomial
bun add ml-regression-polynomial
```

## Health

**Score 50/100 (C)** — status: active.

Positive: esm support; no vulnerabilities.

Warnings: low downloads; no types.

## Facts

| | |
|---|---|
| Version | 4.0.0 |
| Published | 2026-04-18 |
| First published | 2017-04-28 |
| Weekly downloads | 0 |
| License | MIT |
| TypeScript types | none |
| Module format | ESM |
| Dependencies | 2 |
| Unpacked size | 23.2 KB |
| Known vulnerabilities | 0 |
| Install scripts | no |
| GitHub stars | 18 |
| Author | Michaël Zasso |
| Maintainers | stropitek, targos, lpatiny, mljs-bot, maasencioh, jeffersonh44, andcastillo |

## Links

- npm: https://www.npmjs.com/package/ml-regression-polynomial
- Repository: https://github.com/mljs/regression-polynomial
- Homepage: https://github.com/mljs/regression-polynomial#readme
- Issues: https://github.com/mljs/regression-polynomial/issues
- npm.io page: https://npm.io/package/ml-regression-polynomial

## Dependencies (2)

- [ml-matrix](https://npm.io/package/ml-matrix.md) ^6.12.2
- [ml-regression-base](https://npm.io/package/ml-regression-base.md) ^4.0.1

## Recent versions

- 4.0.0 (latest) — 2026-04-18
- 3.0.2 — 2025-05-22
- 3.0.1 — 2024-05-17
- 3.0.0 — 2023-10-02
- 2.2.0 — 2021-06-10
- 2.1.0 — 2020-02-04
- 2.0.0 — 2019-06-29
- 1.0.3 — 2017-07-21
- 1.0.2 — 2017-04-28
- 1.0.1 — 2017-04-28
- 1.0.0 — 2017-04-28

## README

# regression-polynomial

[![NPM version][npm-image]][npm-url]
[![npm download][download-image]][download-url]
[![build status][ci-image]][ci-url]
[![Test coverage][codecov-image]][codecov-url]

Polynomial Regression.

## Installation

`$ npm i ml-regression-polynomial`

## Usage

```js
import { PolynomialRegression } from 'ml-regression-polynomial';

const x = [50, 50, 50, 70, 70, 70, 80, 80, 80, 90, 90, 90, 100, 100, 100];
const y = [
  3.3, 2.8, 2.9, 2.3, 2.6, 2.1, 2.5, 2.9, 2.4, 3.0, 3.1, 2.8, 3.3, 3.5, 3.0,
];
const degree = 5; // setup the maximum degree of the polynomial

const regression = new PolynomialRegression(x, y, degree);

console.log(regression.predict(80)); // Apply the model to some x value. Prints 2.6.
console.log(regression.coefficients); // Prints the coefficients in increasing order of power (from 0 to degree).
console.log(regression.toString(3)); // Prints a human-readable version of the function.
console.log(regression.toLaTeX());
console.log(regression.score(x, y));
```

## Options

An `interceptAtZero` option is available, to force $f(0) = 0$. Also, a "powers array" can be specified.

- Using `interceptAtZero`

```js
const regression = new PolynomialRegression(x, y, degree, {
  interceptAtZero: true,
});
```

- Using the powers array

```js
const powers = [0, 1, 2, 3, 4, 5];
const regression = new PolynomialRegression(x, y, powers);
```

`powers` could also be `[1,2,3,4,5]`or`[1,3,5]` and so on.

For intercepting at zero using an array, skip the zero in the array (the option `interceptAtZero` is ignored in this case.)

## License

[MIT](./LICENSE)

[npm-image]: https://img.shields.io/npm/v/ml-regression-polynomial.svg?style=flat-square
[npm-url]: https://npmjs.org/package/ml-regression-polynomial
[download-image]: https://img.shields.io/npm/dm/ml-regression-polynomial.svg?style=flat-square
[download-url]: https://npmjs.org/package/ml-regression-polynomial
[codecov-image]: https://img.shields.io/codecov/c/github/mljs/regression-polynomial.svg
[codecov-url]: https://codecov.io/gh/mljs/regression-polynomial
[ci-image]: https://github.com/mljs/regression-polynomial/workflows/Node.js%20CI/badge.svg?branch=main
[ci-url]: https://github.com/mljs/regression-polynomial/actions?query=workflow%3A%22Node.js+CI%22

---
_Source: https://npm.io/package/ml-regression-polynomial · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._