Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

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.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

ThunderGBM: Fast GBDTs and Random Forests on GPUs

[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples

A memory efficient GBDT on adaptive distributions. Much faster than LightGBM with higher accuracy. Implicit merge operation.

[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)

GBDT learning + differential privacy. Standalone C++ implementation of "DPBoost" (Li et al.). There are further hardened & SGX versions of the code.

Simple C++ interface for XGBoost（binary classification)

A simple stand-alone version of XGBoost named EasyXGB.

A version of GBDT implementation in python and C++