Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/nuanio/xgboost-node
Run XGBoost model and make predictions in Node.js
https://github.com/nuanio/xgboost-node
algorithm boosting-algorithms gbdt javascript machine-learning nodejs xgboost
Last synced: 24 days ago
JSON representation
Run XGBoost model and make predictions in Node.js
- Host: GitHub
- URL: https://github.com/nuanio/xgboost-node
- Owner: nuanio
- License: other
- Created: 2017-09-09T05:53:46.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2017-10-30T04:06:24.000Z (over 6 years ago)
- Last Synced: 2024-05-05T09:41:43.439Z (about 1 month ago)
- Topics: algorithm, boosting-algorithms, gbdt, javascript, machine-learning, nodejs, xgboost
- Language: Cuda
- Homepage:
- Size: 1.54 MB
- Stars: 38
- Watchers: 6
- Forks: 8
- Open Issues: 3
-
Metadata Files:
- Readme: readme.md
- Changelog: changelog.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
Lists
- AI - xgboost-node - Run XGBoost model and make predictions in Node.js. (Javascript / [Tools](#tools-1))
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (JavaScript / [Tools](#tools-1))
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (JavaScript)
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (Javascript / Speech Recognition)
- awesome-machine-learnings - xgboost-node - Run XGBoost model and make predictions in Node.js. (Javascript / [Tools](#tools-1))
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (Javascript / Speech Recognition)
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (JavaScript / [Tools](#tools-1))
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (Javascript / Speech Recognition)
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (JavaScript / [Tools](#tools-1))
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (Javascript / Tools)
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (Javascript / Speech Recognition)
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (Javascript / Tools)
- awesome-advanced-metering-infrastructure - xgboost-node - Run XGBoost model and make predictions in Node.js. (Javascript / Speech Recognition)
- awesome-machine-learning - xgboost-node - Run XGBoost model and make predictions in Node.js. (Javascript / Speech Recognition)
README
## XGBoost-Node
[](https://travis-ci.org/nuanio/xgboost-node) [](https://www.npmjs.com/package/xgboost) [](https://codecov.io/gh/nuanio/xgboost-node) [](./LICENSE)
eXtreme Gradient Boosting Package in Node.js
XGBoost-Node is a Node.js interface of [XGBoost](https://github.com/dmlc/xgboost). XGBoost is a library from [DMLC](http://dmlc.ml/). It is designed and optimized for boosted trees. The underlying algorithm of XGBoost is an extension of the classic gbm algorithm. With multi-threads and regularization, XGBoost is able to utilize more computational power and get a more accurate prediction.
The package is made to run existing XGBoost model with Node.js easily.
### Features
+ Runs XGBoost Model and make predictions in Node.js.
+ Both dense and sparse matrix input are supported, and missing value is handled.
+ Supports Linux, macOS.
### Install
Install from npm
```bash
npm install xgboost
```
Install from GitHub
```bash
git clone --recursive [email protected]:nuanio/xgboost-node.git
npm install
```
### Documentation
+ [Introduction to XGBoost-Node](./doc/intro.md)
+ [APIs Documentation](./doc/api.md)
+ [Unit Tests Cases](./test/base.js)
+ [中文简介](./doc/intro_zh.md)
### Roadmap
+ [x] Matrix API
+ [x] Model API
+ [x] Prediction API
+ [x] Async API
+ [ ] Windows Support
+ [ ] Training API
+ [ ] Visualization API
### Examples
Train a XGBoost model and save to a file, more in [doc](./doc/intro.md#user-content-usage).
Load the model with XGBoost-Node:
```javascript
const xgboost = require('xgboost');
const model = xgboost.XGModel('iris.xg.model');
const input = new Float32Array([
5.1, 3.5, 1.4, 0.2, // class 0
6.6, 3. , 4.4, 1.4, // class 1
5.9, 3. , 5.1, 1.8 // class 2
]);
const mat = new xgboost.matrix(input, 3, 4);
console.log(model.predict(mat));
// {
// value: [
// 0.991, 0.005, 0.004, // class 0
// 0.004, 0.990, 0.006, // class 1
// 0.005, 0.035, 0.960, // class 2
// ],
// error: undefined, // no error
// }
const errModel = xgboost.XGModel('data/empty');
console.log(errModel);
console.log(errModel.predict());
```
# Contributing
Your help and contribution is very valuable. Welcome to submit issue and pull requests. [Learn more](./.github/CONTRIBUTING.md)