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LightGBM

LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and scalability.

Overview & Gradient-based one-side sampling

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

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Overview

Free and open-sourceGradient-boostingMachine learningMicrosoftDecision tree
21 related topics

Gradient-based one-side sampling

Gradient descent
1 related topics

Key facts & relationships

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

Developers
Microsoft and LightGBM contributors
License
MIT License
Operating system
Windows, macOS, Linux
Original author
Guolin Ke / Microsoft Research
Release
2016; 10 years ago (2016)
Repository
github.com/microsoft/LightGBM

Topics to explore

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

Overview

Gradient-based one-side sampling

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

LightGBM

Nodes25
Edges24
Triples63
Avg. degree1.92
Density0.08
Components1

How this topic connects Entity context

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LightGBM

Top relations

related to Further reading · 28
LightGBM → Andrich, Apress, Butch, Covers XGBoost, Decision Tree, Distributed Deep Learning, Guolin Ke, Highly Efficient Gradient Boosting, ISBN, Keras, Machine Learning, More, Neural Information Processing System, Next-Generation Machine Learning, Packt Publishing, PDF, Python, Qi Meng, Qiwei Ye, Quinto
related to overview · 22
LightGBM → Besides, EFB, Exclusive Feature Bundling, GBDT, GBM, GBRT, GBT, GitHub, GOSS, Gradient-Based One-Side Sampling, Instead, It, Linux, MART, MIT License, Python, RF, The, The LightGBM, Windows
related to External links · 3
LightGBM → GitHub, LightGBMLightGBM, Microsoft Research
Developers · 1
LightGBM → Microsoft and LightGBM contributors
License · 1
LightGBM → MIT License
Operating system · 1
LightGBM → Windows, macOS, Linux
Original author · 1
LightGBM → Guolin Ke / Microsoft Research
Release · 1
LightGBM → 2016; 10 years ago (2016)
Repository · 1
LightGBM → github.com/microsoft/LightGBM
Stable release · 1
LightGBM → v4.3.0 / January 15, 2024; 2 years ago (2024-01-15)

Important terminology Word statistics

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

Important terminology

learning machine data exclusive feature decision tree microsoft features gradient framework python github used gradient-based one-side sampling bundling windows macos

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
LightGBMDevelopersMicrosoft and LightGBM contributors1.00infobox
LightGBMLicenseMIT License1.00infobox
LightGBMOperating systemWindows, macOS, Linux1.00infobox
LightGBMOriginal authorGuolin Ke / Microsoft Research1.00infobox
LightGBMRelease2016; 10 years ago (2016)1.00infobox
LightGBMRepositorygithub.com/microsoft/LightGBM1.00infobox
LightGBMStable releasev4.3.0 / January 15, 2024; 2 years ago (2024-01-15)1.00infobox
LightGBMTypeMachine learning, gradient boosting framework1.00infobox
LightGBMWebsitelightgbm.readthedocs.io1.00infobox
LightGBMWritten inC++, Python, R, C1.00infobox
LightGBMrelated to External linksGitHub0.60section
LightGBMrelated to External linksLightGBMLightGBM0.60section

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

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    Min side: 3
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