XGBoost

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Explore the main themes, entities and connections around XGBoost. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

History

University of Washington Libsvm Perl Kaggle Scikit-learn

8 related topics

Overview

Open-source Software library Regularizing Gradient boosting C++

19 related topics

Features

Newton Boosting Randomization Distributed Out-of-core Feature selection

5 related topics

The algorithm

Taylor approximation

1 related topics

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Developer: The XGBoost Contributors
License: Apache License 2.0
Operating system: Linux, macOS, Microsoft Windows
Release: March 27, 2014; 12 years ago (2014-03-27)
Repository: github.com/dmlc/xgboost
Stable release: 3.0.0 / 15 March 2025; 17 months ago (15 March 2025)

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Open-source Open-source software
Software library Library (computing)
Regularizing Regularization (mathematics)
Gradient boosting
C++
Java Java (programming language)
Python Python (programming language)
Julia Julia (programming language)
Perl Perl (programming language)
Scala Scala (programming language)
Linux
Microsoft Windows
MacOS
Apache Hadoop
Apache Spark
Apache Flink
Dask Dask (software)
Machine learning
Competitions Competitive programming

History

Features

Newton Boosting Newton's method in optimization
Randomization
Distributed Distributed computing
Out-of-core
Feature selection

The algorithm

Taylor approximation Taylor series

Awards

John Chambers John Chambers (statistician)

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Nodes40

Edges39

Triples50

Avg. degree1.95

Density0.05

Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

XGBoost

Top relations

related to history · 29

XGBoost → An, Apache Flink, Apache Hadoop, Apache Spark, Carlos Guestrin, Data Flow, Deep, Distributed, DMLC, FPGAs, Higgs Machine Learning Challenge, Initially, It, Java, Julia, Kaggle, Machine Learning Community, ML, OpenCL, Perl

related to Parameters · 5

XGBoost → Gamma, Lagrange, Learning, Some, The

related to Features · 4

XGBoost → BoostingExtra, Clever, Salient, Theoretically

related to The algorithm · 3

XGBoost → Newton, Raphson, Taylor

Developer · 1

XGBoost → The XGBoost Contributors

License · 1

XGBoost → Apache License 2.0

Operating system · 1

XGBoost → Linux, macOS, Microsoft Windows

Release · 1

XGBoost → March 27, 2014; 12 years ago (2014-03-27)

Repository · 1

XGBoost → github.com/dmlc/xgboost

Stable release · 1

XGBoost → 3.0.0 / 15 March 2025; 17 months ago (15 March 2025)

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

displaystyle hat learning machine needed distributed tree algorithm arg min clarification left right boosting also number model training gradient library

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
XGBoost	Developer	The XGBoost Contributors	1.00	infobox
XGBoost	License	Apache License 2.0	1.00	infobox
XGBoost	Operating system	Linux, macOS, Microsoft Windows	1.00	infobox
XGBoost	Release	March 27, 2014; 12 years ago (2014-03-27)	1.00	infobox
XGBoost	Repository	github.com/dmlc/xgboost	1.00	infobox
XGBoost	Stable release	3.0.0 / 15 March 2025; 17 months ago (15 March 2025)	1.00	infobox
XGBoost	Type	Machine learning	1.00	infobox
XGBoost	Website	xgboost.ai	1.00	infobox
XGBoost	Written in	C++	1.00	infobox
XGBoost	related to Features	Salient	0.60	section
XGBoost	related to Features	Clever	0.60	section
XGBoost	related to Features	BoostingExtra	0.60	section

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

Connections between topic areas Semantic bridges

Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

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