https://github.com/fractal-solutions/xgboost-js
A pure JavaScript implementation of XGBoost for both Node.js and browser environments.
https://github.com/fractal-solutions/xgboost-js
ai artificial-intelligence bun bun-js deno javascript machine-learning nodejs npm supervised-learning xgboost xgboost-model
Last synced: 9 months ago
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A pure JavaScript implementation of XGBoost for both Node.js and browser environments.
- Host: GitHub
- URL: https://github.com/fractal-solutions/xgboost-js
- Owner: fractal-solutions
- License: mit
- Created: 2024-11-28T09:22:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-14T02:22:39.000Z (over 1 year ago)
- Last Synced: 2025-09-15T21:45:00.886Z (9 months ago)
- Topics: ai, artificial-intelligence, bun, bun-js, deno, javascript, machine-learning, nodejs, npm, supervised-learning, xgboost, xgboost-model
- Language: JavaScript
- Homepage: https://www.npmjs.com/package/@fractal-solutions/xgboost-js
- Size: 17.6 KB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# xgboost-js
A pure JavaScript implementation of XGBoost for both Node.js and browser environments.
## Installation
```bash
npm install @fractal-solutions/xgboost-js
```
## Usage
```javascript
const { XGBoost } = require('@fractal-solutions/xgboost-js');
// Initialize the model
const model = new XGBoost({
learningRate: 0.3,
maxDepth: 4,
minChildWeight: 1,
numRounds: 100
});
// Train the model
model.fit(X_train, y_train);
// Make predictions
const predictions = model.predictBatch(X_test);
```
## Features
- Binary classification
- Model serialization
- Feature importance calculation
- Comprehensive test suite
- Pure JavaScript implementation
- Works in both Node.js and browser environments
## Documentation
For full documentation, see [docs/xgboost.md](docs/xgboost.md)
## License
MIT © Fractal Solutions