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Light Gradient Boosting Machine logo.

Welcome to LightGBM's documentation!

LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:

  • Faster training speed and higher efficiency.
  • Lower memory usage.
  • Better accuracy.
  • Support of parallel, distributed, and GPU learning.
  • Capable of handling large-scale data.

For more details, please refer to Features.

.. toctree::
   :maxdepth: 1
   :caption: Contents:

   Installation Guide <Installation-Guide>
   Quick Start <Quick-Start>
   Python Quick Start <Python-Intro>
   Features <Features>
   Experiments <Experiments>
   Parameters <Parameters>
   Parameters Tuning <Parameters-Tuning>
   C API <C-API>
   Python API <Python-API>
   R API <>
   Distributed Learning Guide <Parallel-Learning-Guide>
   GPU Tutorial <GPU-Tutorial>
   Advanced Topics <Advanced-Topics>
   FAQ <FAQ>
   Development Guide <Development-Guide>

.. toctree::


Indices and Tables