CatBoost
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Overview
HEALTH SCORE
Fair
53out of 100
Security posture drags the score down. Maintainer coverage and development cadence help keep it from falling further.
LIFECYCLE
Active
Consistent commits, responsive maintainers, regular releases, and healthy issue triage.
Health breakdown
Fair(53/100)
Maintainer availability, known risks and supply chain posture, and whether the project is actively developed or appropriately stable.
Maintainer responsiveness
Median response time is 7 days.
Bus factor
5 active maintainers currently have merge rights.
Organization Diversity
Contributors span 119 organizations.
Project Achievements
Leaderboards
About
CatBoost is a high-performance, open-source gradient boosting library developed by Yandex that implements gradient boosting on decision trees. It provides fast, scalable, and accurate machine learning algorithms for classification, regression, and ranking tasks, with built-in support for categorical features.
First commit
Software value
$244M
Tags
big-data
catboost
categorical-features
coreml
cuda
data-mining
data-science
decision-trees
gbdt
gbm
gpu
gpu-computing
gradient-boosting
kaggle
machine-learning
python
r
tutorial
License(s)
Apache-2.0
Data sources
Git
GitHub