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2014-02-06 21:26:27 -08:00
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xgboost

Creater: Tianqi Chen: tianqi.tchen AT gmail

General Purpose Gradient Boosting Library

Intention: A stand-alone efficient library to do machine learning in functional space

Planned key components (TODO):

(1) Gradient boosting models: - regression tree - linear model/lasso (2) Objectives to support tasks: - regression - classification - ranking - matrix factorization - structured prediction (3) OpenMP support for parallelization(optional)

Description
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
Readme 33 MiB
Languages
C++ 45.5%
Python 20.3%
Cuda 15.2%
R 6.8%
Scala 6.4%
Other 5.6%