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Tool

xgboost

by dmlc ID: gh-tool--dmlc--xgboost

eXtreme Gradient Boosting =========== Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***. It implements machine learning algorithms under the Gradi...

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C++ Lang

Open Source 28.0K Stars

1.0.0 Version

Alpha Reliability

Tool Information Summary
Entity Passport
Registry ID	gh-tool--dmlc--xgboost
Provider	github

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Cite this tool

Academic & Research Attribution

BibTeX

@misc{gh_tool__dmlc__xgboost,
  author = {dmlc},
  title = {xgboost Tool},
  year = {2026},
  howpublished = {\url{https://github.com/dmlc/xgboost}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

dmlc. (2026). xgboost [Tool]. Free2AITools. https://github.com/dmlc/xgboost

🔬Technical Deep Dive

Full Specifications [+]

⚡ Quick Commands

🐙 GitHub Clone

git clone https://github.com/dmlc/xgboost

🐙 Git Clone

git clone https://github.com/dmlc/xgboost

⚖️ Free2AI Nexus Index

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0.0

Top 19% Overall Impact

🔥 Popularity (P) 0

🚀 Velocity (V) 0

🛡️ Credibility (C) 0

🔧 Utility (U) 0

Nexus Verified Data

💬 Why this score?

The Nexus Index for xgboost aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.

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📊 P: HuggingFace Stats 📈 V: 7-Day Delta 📄 C: Papers With Code 🔧 U: Deploy Score

Data Verified 🕐 Last Updated: Not calculated

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📋 Specs

Language: C++
License: Open Source
Version: 1.0.0

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Usage documentation not yet indexed for this tool.

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Technical Documentation

eXtreme Gradient Boosting

Community |
Documentation |
Resources |
Contributors |
Release Notes

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 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 (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples.

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.

Reference

Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
XGBoost originates from research project at University of Washington.