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
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Updated
Dec 21, 2024 - C++
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.
Train Gradient Boosting models that are both high-performance *and* Fair!
[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
A powerful tree-based uplift modeling system.
[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)
Raspberry PI as Newtek NDI monitor
(i) Identify and extract mean reversion, (swing points) data points from non-stationary data, (ii) generate interpretable rules to predict such data points (iii) using supervised machine learning classification models in R such as GBM and RF.
C++ Market Simulator using real stock market data from S&P500 composed of a User Portfolio and a Market Simulator.
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