# roc-multiclass

**Repository Path**: mirrors_mljs/roc-multiclass

## Basic Information

- **Project Name**: roc-multiclass
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-22
- **Last Updated**: 2026-02-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ROC - Receiver Operating Characteristic for multiple class

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

A receiver operating characteristic (ROC) curve aims to summarize the performance of a binary classifier system as its discrimination threshold is varied. The multiple class option allows to handle systems with more than two classes.

<p align="center">
  <img src="image/readme-example.png">
</p>

## Installation

`$ npm i ml-roc-multiclass`

## Usage

```js
import { getRocCurve, getAuc, getClasses } from 'ml-roc-multiclass';

const targets = [
  'class1',
  'class1',
  'class1',
  'class1',
  'class2',
  'class2',
  'class2',
  'class2',
];
const predictions = [0.95, 0.15, 0.13, 0.08, 0.93, 0.91, 1.99, 0.12];

const classes = getClasses(targets);
// console.log(classes);
// [
//     {
//         "class":"class1",
//         "value":0,
//         "ids":[0,1,2,3]
//     },
//     {
//         "class":"class2",
//         "value":1,
//         "ids":[4,5,6,7]
//     }
// ]

const curve = getRocCurve(targets, predictions);

// console.log(curve);

// [
//     {
//         "sensitivities": [1, 1, 0.75, 0.75, 0.75, 0.5, 0.25, 0.25, 0],
//         "specificities": [0, 0.25, 0.25, 0.5, 0.75, 0.75, 0.75, 1, 1]
//     }
// ]

const auc = getAuc(curve);
console.log(auc); // 0.6875
```

## [API Documentation](https://mljs.github.io/roc-multiclass/)

## References

- Bewick, V., Cheek, L., & Ball, J. (2004). Statistics review 13: receiver operating characteristic curves. Critical care, 8(6), 1-5.

- Hand, D. J., & Till, R. J. (2001). A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine learning, 45(2), 171-186.

- [https://en.wikipedia.org/wiki/Receiver_operating_characteristic](https://en.wikipedia.org/wiki/Receiver_operating_characteristic).

## License

[MIT](./LICENSE)

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