# xgboost

**Repository Path**: caliburnus/xgboost

## Basic Information

- **Project Name**: xgboost
- **Description**: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library,  for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
- **Primary Language**: C++
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2018-08-15
- **Last Updated**: 2024-07-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/xgboost.png width=135/>  eXtreme Gradient Boosting
===========
[![Build Status](https://travis-ci.org/dmlc/xgboost.svg?branch=master)](https://travis-ci.org/dmlc/xgboost)
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[Community](https://xgboost.ai/community) |
[Documentation](https://xgboost.readthedocs.org) |
[Resources](demo/README.md) |
[Contributors](CONTRIBUTORS.md) |
[Release Notes](NEWS.md)

XGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***.
It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework.
XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.

License
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© Contributors, 2016. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.

Contribute to XGBoost
---------------------
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
Checkout the [Community Page](https://xgboost.ai/community)

Reference
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- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](http://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.