LightGBM
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Overview
HEALTH SCORE
Excellent*
Partial score (2/3 categories)
58out of 65
Strong maintainer coverage. Development cadence is the only remaining gap. Security & Supply Chain is unavailable, so weights were redistributed.
LIFECYCLE
Active
Consistent commits, responsive maintainers, regular releases, and healthy issue triage.
Health breakdown
Excellent*(58/65)
Maintainer availability, known risks and supply chain posture, and whether the project is actively developed or appropriately stable.
Maintainer responsiveness
Median response time is 1 day.
Bus factor
7 active maintainers currently have merge rights.
Organization Diversity
Contributors span 128 organizations.
Project Achievements
Leaderboards
About
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with faster training speed and higher efficiency, lower memory usage, better accuracy, parallel and GPU learning, and handling large-scale data.
First commit
Software value
$3.5M
Tags
data-mining
decision-trees
distributed
gbdt
gbm
gbrt
gradient-boosting
kaggle
lightgbm
machine-learning
microsoft
parallel
python
r
License(s)
MIT
Data sources
Git
GitHub