LightGBM, Light Gradient Boosting Machine
=========================================
[![Build Status](https://travis-ci.org/Microsoft/LightGBM.svg?branch=master)](https://travis-ci.org/Microsoft/LightGBM)
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
- Parallel and GPU learning supported
- Capable of handling large-scale data
For more details, please refer to [Features](https://github.com/Microsoft/LightGBM/wiki/Features).
[Experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
News
----
04/10/2017 : LightGBM now supports GPU-accelerated tree learning. Please read our [GPU Tutorial](./docs/GPU-Tutorial.md) and [Performance Comparison](./docs/GPU-Performance.md).
02/20/2017 : Update to LightGBM v2.
01/08/2017 : Release [**R-package**](./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.
12/02/2016 : Release [**python-package**](./python-package) beta version, welcome to have a try and provide feedback.
External(unofficial) Repo
-------------------------
Julia Package: https://github.com/Allardvm/LightGBM.jl
JPMML: https://github.com/jpmml/jpmml-lightgbm
Get Started And Documents
-------------------------
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).
* [**Wiki**](https://github.com/Microsoft/LightGBM/wiki)
* [**Installation Guide**](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide)
* [**Quick Start**](https://github.com/Microsoft/LightGBM/wiki/Quick-Start)
* [**Examples**](https://github.com/Microsoft/LightGBM/tree/master/examples)
* [**Features**](https://github.com/Microsoft/LightGBM/wiki/Features)
* [**Parallel Learning Guide**](https://github.com/Microsoft/LightGBM/wiki/Parallel-Learning-Guide)
* [**GPU Learning Tutorial**](./docs/GPU-Tutorial.md)
* [**Configuration**](https://github.com/Microsoft/LightGBM/wiki/Configuration)
* [**Document Indexer**](https://github.com/Microsoft/LightGBM/blob/master/docs/Readme.md)
How to Contribute
-----------------
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
- Check out [call for contributions](https://github.com/Microsoft/LightGBM/issues?q=is%3Aissue+is%3Aopen+label%3Acall-for-contribution) to see what can be improved, or open an issue if you want something.
- 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](./docs/development.md).
- Open issue if you met problems during development.
Microsoft Open Source Code of Conduct
------------
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
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lightGBM vs2015 win10 环境下编译好的包 (522个子文件)
00Index 423B
00Index 423B
histogram16.cl 42KB
histogram16.cl 42KB
histogram256.cl 35KB
histogram256.cl 35KB
histogram64.cl 33KB
histogram64.cl 33KB
win_build_package.cmd 180B
win_build_package.cmd 180B
train.conf 3KB
train.conf 3KB
train.conf 3KB
train.conf 3KB
train.conf 3KB
train.conf 3KB
train.conf 3KB
train.conf 3KB
train.conf 1KB
train.conf 1KB
predict.conf 73B
predict.conf 73B
predict.conf 72B
predict.conf 72B
predict.conf 70B
predict.conf 70B
predict.conf 69B
predict.conf 69B
predict.conf 67B
predict.conf 67B
gpu_tree_learner.cpp 52KB
gpu_tree_learner.cpp 52KB
c_api.cpp 47KB
c_api.cpp 47KB
dataset_loader.cpp 41KB
dataset_loader.cpp 41KB
gbdt.cpp 33KB
gbdt.cpp 33KB
serial_tree_learner.cpp 23KB
serial_tree_learner.cpp 23KB
dataset.cpp 21KB
dataset.cpp 21KB
voting_parallel_tree_learner.cpp 21KB
voting_parallel_tree_learner.cpp 21KB
metadata.cpp 18KB
metadata.cpp 18KB
tree.cpp 17KB
tree.cpp 17KB
lightgbm_R.cpp 17KB
lightgbm_R.cpp 17KB
config.cpp 15KB
config.cpp 15KB
bin.cpp 12KB
bin.cpp 12KB
data_parallel_tree_learner.cpp 11KB
data_parallel_tree_learner.cpp 11KB
application.cpp 10KB
application.cpp 10KB
network.cpp 7KB
network.cpp 7KB
linkers_socket.cpp 7KB
linkers_socket.cpp 7KB
linker_topo.cpp 6KB
linker_topo.cpp 6KB
dcg_calculator.cpp 4KB
dcg_calculator.cpp 4KB
parser.cpp 4KB
parser.cpp 4KB
feature_parallel_tree_learner.cpp 3KB
feature_parallel_tree_learner.cpp 3KB
objective_function.cpp 2KB
objective_function.cpp 2KB
boosting.cpp 2KB
boosting.cpp 2KB
metric.cpp 2KB
metric.cpp 2KB
tree_learner.cpp 1KB
tree_learner.cpp 1KB
lightgbm-all.cpp 1KB
lightgbm-all.cpp 1KB
lightgbm-fullcode.cpp 1001B
lightgbm-fullcode.cpp 1001B
linkers_mpi.cpp 811B
linkers_mpi.cpp 811B
main.cpp 526B
main.cpp 526B
LightGBM.VC.db 31.52MB
LightGBM.VC.db 31.52MB
DESCRIPTION 1KB
DESCRIPTION 1KB
lib_lightgbm.dll 703KB
lib_lightgbm.dll 703KB
dockerfile-python 1KB
dockerfile-python 1KB
lightgbm-0.1-py3.6.egg 357KB
lightgbm-0.1-py3.6.egg 357KB
lib_lightgbm.exp 23KB
lib_lightgbm.exp 23KB
LightGBM.vcxproj.filters 10KB
LightGBM.vcxproj.filters 10KB
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