sb-ai-lab/Py-Boost

Multivariate Boosted TRee

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.

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

H2Oai GPU Edition

A lightweight gradient boosted decision tree package.

LinearBoost Classifier is a rapid and accurate classification algorithm that builds upon a very fast, linear classifier.

(ICLR 2024) GRANDE: Gradient-Based Decision Tree Ensembles

Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application