XGBoost
Sign in to saveAlso known as eXtreme Gradient Boosting
XGBoost (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python, R, Julia, Perl, and Scala. It works on Linux, Microsoft Windows, and macOS. From the project description, it aims to provide a "Scalable, Portable and Distributed Gradient Boosting (GBM, GBRT, GBDT) Library". It runs on a single machine, as well as the distributed processing frameworks Apache Hadoop, Apache Spark, Apache Flink, and Dask.
Key facts
- Software.name
- XGBoost
- Software.logo
- XGBoost_logo.svg
- Software.developer
- The XGBoost Contributors
- Software.operating system
- Linux, macOS, Microsoft Windows
- Software.programming language
- C++
- Software.genre
- Machine learning
- Software.license
- Apache License 2.0
via Wikipedia infobox
Source code
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. 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 (
Excerpt from the source-code README · 4,575 chars · not written by Vinony
Wikidata facts
- Official website
- xgboost.ai
Show 3 more facts
- source code repository URL
- github.com/dmlc/xgboost
- software version identifier
- 3.1.3
- Stack Exchange tag
- datascience.stackexchange.com/tags/xgboost
Sources (8)
via Wikidata · CC0
~6 min read
Article
7 sectionsContents
- History
- Features
- The algorithm
- Parameters
- Awards
- See also
- References
XGBoost (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python, R, Julia, Perl, and Scala. It works on Linux, Microsoft Windows, and macOS. From the project description, it aims to provide a "Scalable, Portable and Distributed Gradient Boosting (GBM, GBRT, GBDT) Library". It runs on a single machine, as well as the distributed processing frameworks Apache Hadoop, Apache Spark, Apache Flink, and Dask.
XGBoost gained much popularity and attention in the mid-2010s as the algorithm of choice for many winning teams of machine learning competitions.