npm install xgboost   
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.
+ 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 from npm
``bash`
npm install xgboost
Install from GitHub
`bash`
git clone --recursive git@github.com:nuanio/xgboost-node.git
npm install
+ Introduction to XGBoost-Node
+ 中文简介
+ [x] Matrix API
+ [x] Model API
+ [x] Prediction API
+ [x] Async API
+ [ ] Windows Support
+ [ ] Training API
+ [ ] Visualization API
Train a XGBoost model and save to a file, more in doc.
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());
``
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