LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
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@@ -39,7 +39,7 @@ News
01/08/2017 : Release [**R-package**](https://github.com/Microsoft/LightGBM/tree/master/R-package) beta version, welcome to have a try and provide feedback.
12/05/2016 : **Categorical Features as input directly**(without one-hot coding). Experiment on [Expo data](http://stat-computing.org/dataexpo/2009/) shows about 8x speed-up with same accuracy compared with one-hot coding.
12/05/2016 : **Categorical Features as input directly**(without one-hot coding). Experiment on [Expo data](http://stat-computing.org/dataexpo/2009/) shows about 8x speed-up with same accuracy compared with one-hot coding.
12/02/2016 : Release [**python-package**](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide feedback.
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@@ -52,37 +52,31 @@ Julia Package: https://github.com/Allardvm/LightGBM.jl
JPMML: https://github.com/jpmml/jpmml-lightgbm
Get Started And Documents
Get Started And Documentation
-------------------------
To get started, please follow the [Installation Guide](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide) and[Quick Start](https://github.com/Microsoft/LightGBM/wiki/Quick-Start).
Install by following the guide for the [command line program](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide), [Python package](https://github.com/Microsoft/LightGBM/tree/master/python-package) or [R-package](https://github.com/Microsoft/LightGBM/tree/master/R-package). Then please see the[Quick Start](https://github.com/Microsoft/LightGBM/wiki/Quick-Start) guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
External Links
--------------
Useful if you are looking for details:
Next you will want to read:
*[**Read The Docs**](http://lightgbm.readthedocs.io/en/latest/) for an all in one documentation from this repository in a browsable fashion
*[**Laurae++ interactive documentation**](https://sites.google.com/view/lauraepp/parameters) for an interactive and detailed documentation on hyperparameters
*[**Examples**](https://github.com/Microsoft/LightGBM/tree/master/examples) showing command line usage of common tasks
*[**Features**](https://github.com/Microsoft/LightGBM/wiki/Features) and algorithms supported by LightGBM
*[**Parameters**](https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.md) is an exhaustive list of customization you can make
*[**Parallel Learning**](https://github.com/Microsoft/LightGBM/wiki/Parallel-Learning-Guide) and [**GPU Learning**](https://github.com/Microsoft/LightGBM/blob/master/docs/GPU-Tutorial.md) can speed up computation
*[**Laurae++ interactive documentation**](https://sites.google.com/view/lauraepp/parameters) is a detailed guide for hyperparameters
Support
-------
You can ask questions and join the development discussion on:
*[**How we Update readthedocs.io**](https://github.com/Microsoft/LightGBM/blob/master/docs/README.md)
*Check out the [Development Guide](https://github.com/Microsoft/LightGBM/blob/master/docs/development.rst).
Support
-------
You can also create **bug reports and feature requests** (not including questions) in [Github issues](https://github.com/Microsoft/LightGBM/issues).
* Ask a question [on Stack Overflow with the `lightgbm` tag ](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions.
* Discuss on the [LightGBM Gitter](https://gitter.im/Microsoft/LightGBM).
* Open **bug reports** and **feature requests** (not questions) on [Github issues](https://github.com/Microsoft/LightGBM/issues).
How to Contribute
-----------------
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@@ -93,7 +87,6 @@ LightGBM has been developed and used by many active community members. Your help
- Contribute to the [tests](https://github.com/Microsoft/LightGBM/tree/master/tests) to make it more reliable.
- Contribute to the [documents](https://github.com/Microsoft/LightGBM/tree/master/docs) to make it clearer for everyone.
- Contribute to the [examples](https://github.com/Microsoft/LightGBM/tree/master/examples) to share your experience with other users.
- Check out [Development Guide](https://github.com/Microsoft/LightGBM/blob/master/docs/development.rst).
- Open issue if you met problems during development.
Refer to `Features <https://github.com/Microsoft/LightGBM/wiki/Features>`__ to get important algorithms used in LightGBM.
Refer to `Features <https://github.com/Microsoft/LightGBM/wiki/Features>`__ to understand important algorithms used in LightGBM.
Classes and Code Structure
--------------------------
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@@ -78,7 +78,7 @@ Refere to the comments in `c\_api.h <https://github.com/Microsoft/LightGBM/blob/
High Level Language Package
---------------------------
Follow the implementation of `python-package <https://github.com/Microsoft/LightGBM/tree/master/python-package/lightgbm>`__.
See the implementations at `python-package <https://github.com/Microsoft/LightGBM/tree/master/python-package/lightgbm>`__ and `R-package <https://github.com/Microsoft/LightGBM/tree/master/R-package>`__.
