896aede340
* Split up installation and building from source. * Use consistent section titles. Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
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1.1 KiB
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38 lines
1.1 KiB
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#####################
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XGBoost Documentation
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#####################
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**XGBoost** is an optimized distributed gradient boosting library designed to be highly **efficient**, **flexible** and **portable**.
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It implements machine learning algorithms under the `Gradient Boosting <https://en.wikipedia.org/wiki/Gradient_boosting>`_ framework.
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XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
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The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
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********
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Contents
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********
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.. toctree::
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:maxdepth: 2
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:titlesonly:
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install
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build
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get_started
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tutorials/index
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faq
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XGBoost User Forum <https://discuss.xgboost.ai>
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GPU support <gpu/index>
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parameter
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prediction
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treemethod
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Python package <python/index>
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R package <R-package/index>
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JVM package <jvm/index>
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Ruby package <https://github.com/ankane/xgb>
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Swift package <https://github.com/kongzii/SwiftXGBoost>
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Julia package <julia>
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C Package <c>
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C++ Interface <c++>
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CLI interface <cli>
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contrib/index
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