🛠ī¸
xgboost tool by dmlc
⭐ 49.3
🛠ī¸
Tool

xgboost

by dmlc gh-tool--dmlc--xgboost
Nexus Index
49.3 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 79
R: Recency 100
Q: Quality 50
Tech Context
Vital Performance
0 DL / 30D
0.0%
Python Lang
Open Source 28.2K Stars
1.0.0 Version
Alpha Reliability
Tool Information Summary
Entity Passport
Registry ID gh-tool--dmlc--xgboost
License Apache-2.0
Provider github
📜

Cite this tool

Academic & Research Attribution

BibTeX 
@misc{gh_tool__dmlc__xgboost,
  author = {dmlc},
  title = {xgboost Tool},
  year = {2026},
  howpublished = {\url{https://free2aitools.com/tool/gh-tool--dmlc--xgboost}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
dmlc. (2026). xgboost [Tool]. Free2AITools. https://free2aitools.com/tool/gh-tool--dmlc--xgboost

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🐍 PIP Install
pip install xgboost

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
49.3
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 79
Recency (R) 100
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for xgboost: Semantic (S:50), Authority (A:0), Popularity (P:79), Recency (R:100), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live

📋 Specs

Language
Python
License
Apache-2.0
Version
1.0.0
đŸ“Ļ

Usage documentation not yet indexed for this tool.

Technical Documentation

eXtreme Gradient Boosting

XGBoost-CI Documentation Status GitHub license CRAN Status Badge PyPI version Conda version Optuna Twitter OpenSSF Scorecard Open In Colab

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

Š Contributors, 2021. Licensed under an Apache-2 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.

Sponsors

Become a sponsor and get a logo here. See details at Sponsoring the XGBoost Project. The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).

Open Source Collective sponsors

Backers on Open Collective Sponsors on Open Collective

Sponsors

[Become a sponsor]

NVIDIA

Backers

[Become a backer]

Social Proof

GitHub Repository
28.2KStars
🐙 Data Source: GitHub ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on GitHub metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Tool Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
gh-tool--dmlc--xgboost
slug
dmlc--xgboost
source
github
author
dmlc
license
Apache-2.0
tags
distributed-systems, gbdt, gbm, gbrt, machine-learning, xgboost, c++

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
other

📊 Engagement & Metrics

downloads
0
stars
28,209
forks
0
github stars
28,209

Data indexed from public sources. Updated daily.