lightgbm-org/LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Implements leaf-wise tree growth with histogram-based learning to reduce memory footprint and accelerate training on CPU and GPU hardware. Provides native bindings for Python, R, and C++, with ecosystem integrations including FLAML for AutoML, Optuna for hyperparameter tuning, and model compilers like Treelite and Hummingbird for production deployment.
18,157 stars. Actively maintained with 15 commits in the last 30 days.
Stars
18,157
Forks
3,988
Language
C++
License
MIT
Category
Last pushed
Mar 13, 2026
Commits (30d)
15
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/lightgbm-org/LightGBM"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related frameworks
dmlc/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python,...
catboost/catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for...
stanfordmlgroup/ngboost
Natural Gradient Boosting for Probabilistic Prediction
fabsig/GPBoost
Tree-Boosting, Gaussian Processes, and Mixed-Effects Models
serengil/chefboost
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and...