A simple stand-alone version of XGBoost named EasyXGB.
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Updated
Aug 30, 2018 - C++
A simple stand-alone version of XGBoost named EasyXGB.
Simple C++ interface for XGBoost(binary classification)
[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)
A version of GBDT implementation in python and C++
A memory efficient GBDT on adaptive distributions. Much faster than LightGBM with higher accuracy. Implicit merge operation.
GBDT learning + differential privacy. Standalone C++ implementation of "DPBoost" (Li et al.). There are further hardened & SGX versions of the code.
[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
ThunderGBM: Fast GBDTs and Random Forests on GPUs
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.
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