LightGBM and awesome-gradient-boosting-papers
One is a high-performance gradient boosting framework for machine learning tasks, and the other is a curated list of research papers about gradient boosting with implementations, making them complements where the latter can inform the use and development related to the former.
About LightGBM
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.
About awesome-gradient-boosting-papers
benedekrozemberczki/awesome-gradient-boosting-papers
A curated list of gradient boosting research papers with implementations.
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