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, Flink and DataFlow
C++ R Scala Python Java Cuda Other
Latest commit b4d97d3 Feb 20, 2017 @khotilov khotilov committed with tqchen R maintenance Feb2017 (#2045)
* [R] better argument check in xgb.DMatrix; fixes #1480

* [R] showsd was a dummy; fixes #2044

* [R] better categorical encoding explanation in vignette; fixes #1989

* [R] new roxygen version docs update
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R-package R maintenance Feb2017 (#2045) Feb 20, 2017
amalgamation Histogram Optimized Tree Grower (#1940) Jan 13, 2017
demo Fix comment in (#1923) Jan 2, 2017
dmlc-core @ 78b78be Changing omp_get_num_threads to omp_get_max_threads (#1831) Dec 4, 2016
doc Fix typo in Python Package Introduction (#2023) Feb 9, 2017
include/xgboost Histogram Optimized Tree Grower (#1940) Jan 13, 2017
jvm-packages [jvm-packages] Bump spark to 2.1 (#2046) Feb 19, 2017
make Set TEST_COVER to 0 by default (#1853) Dec 11, 2016
plugin Fix cmake build for linux. Update GPU benchmarks. (#1904) Dec 23, 2016
python-package A fix regarding the compatibility with python 2.6 (#1981) Jan 30, 2017
rabit @ a9a2a69 Fix warnings from g++5 or higher (#1510) Aug 26, 2016
src [trivial] Fix typo in Poisson metric name. (#2026) Feb 9, 2017
tests Histogram Optimized Tree Grower (#1940) Jan 13, 2017
.gitignore Add make commands for tests Dec 4, 2016
.gitmodules [REFACTOR] cleanup structure Jan 16, 2016
.travis.yml travis: Add code coverage on success Dec 4, 2016
CMakeLists.txt Fix cmake build for linux. Update GPU benchmarks. (#1904) Dec 23, 2016 Use bst_float consistently throughout (#1824) Nov 30, 2016 issue template (#1475) Aug 18, 2016
LICENSE update year in LICENSE, and files Mar 15, 2016
Makefile autoconf for solaris (#1880) Dec 16, 2016 [CORE] Refactor cache mechanism (#1540) Sep 3, 2016 change contribution link to open issues (#1834) Dec 2, 2016
appveyor.yml GPU plug-in improvements + basic Windows continuous integration (#1752) Nov 10, 2016 Minor fix on installation guide and (the probably deprecated) build s… Feb 24, 2016

eXtreme Gradient Boosting

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XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.

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XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.


© Contributors, 2016. Licensed under an Apache-2 license.