XGBoost
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Overview
HEALTH SCORE
Excellent
85out of 100
Strong maintainer coverage and development cadence. Security posture is the only remaining gap.
LIFECYCLE
Active
Consistent commits, responsive maintainers, regular releases, and healthy issue triage.
Health breakdown
Excellent(85/100)
Maintainer availability, known risks and supply chain posture, and whether the project is actively developed or appropriately stable.
Maintainer responsiveness
Median response time is 0 days.
Bus factor
22 active maintainers currently have merge rights.
Organization Diversity
Contributors span 270 organizations.
Project Achievements
Leaderboards
About
XGBoost is a scalable, distributed gradient boosting library that provides parallel tree boosting for machine learning tasks. It implements machine learning algorithms under the gradient boosting framework, offering high performance, flexibility and portability across multiple programming languages and platforms.
First commit
Software value
$6.6M
Tags
distributed-systems
gbdt
gbm
gbrt
machine-learning
xgboost
License(s)
Apache-2.0
Data sources
Git
GitHub