Xgboost
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Overview
Tagline Brand's website · 2026-10-04
Exceptional speed, accuracy, and scalability for gradient boosting, with support for major distributed environments and bindings in multiple popular programming languages (Python, R, Java/JVM, Ruby, Swift, Julia, C, C++).
Description Brand's website · 2026-10-04
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, providing parallel tree boosting to solve many data science problems quickly and accurately. It runs on major distributed environments like Hadoop, SGE, and MPI, capable of handling problems beyond billions of examples.
Offerings
Core capabilities Brand's website · 2026-10-04
- An optimized
- distributed gradient boosting library providing parallel tree boosting algorithms.
Pricing Brand's website · 2026-10-04
Open-source; free to use under an open-source license.
Audience
Target industry Brand's website · 2026-10-04
Data scientists, machine learning engineers, researchers, and developers working on predictive modeling, large-scale data analysis, and high-performance machine learning tasks across various industries.
Landscape
Competitors Brand's website · 2026-10-04
- LightGBM
- CatBoost
- Scikit-learn (Gradient Boosting)
- H2O.ai (H2O-3 GBM)
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Claim Xgboost — free →Page last updated 2026-10-04.