XGBoost-Node

eXtreme Gradient Boosting Package in Node.js

XGBoost-Node is a Node.js interface of XGBoost. XGBoost is a library from DMLC. 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

npm install xgboost

Install from GitHub

git clone --recursive git@github.com:nuanio/xgboost-node.git
npm install

Documentation

Roadmap

Examples

Train a XGBoost model and save to a file, more in doc.

Load the model with XGBoost-Node:

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