LightGBM and GPBoost
GPBoost extends LightGBM by incorporating Gaussian processes and mixed-effects models, making them complementary tools where GPBoost builds upon and enhances LightGBM's core capabilities.
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 GPBoost
fabsig/GPBoost
Tree-Boosting, Gaussian Processes, and Mixed-Effects Models
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