LightGBM, Light Gradient Boosting Machine
=========================================
[![Azure Pipelines Build Status](https://lightgbm-ci.visualstudio.com/lightgbm-ci/_apis/build/status/Microsoft.LightGBM?branchName=master)](https://lightgbm-ci.visualstudio.com/lightgbm-ci/_build/latest?definitionId=1)
[![Appveyor Build Status](https://ci.appveyor.com/api/projects/status/1ys5ot401m0fep6l/branch/master?svg=true)](https://ci.appveyor.com/project/guolinke/lightgbm/branch/master)
[![Travis Build Status](https://travis-ci.org/Microsoft/LightGBM.svg?branch=master)](https://travis-ci.org/Microsoft/LightGBM)
[![Documentation Status](https://readthedocs.org/projects/lightgbm/badge/?version=latest)](https://lightgbm.readthedocs.io/)
[![GitHub Issues](https://img.shields.io/github/issues/Microsoft/LightGBM.svg)](https://github.com/Microsoft/LightGBM/issues)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/Microsoft/LightGBM/blob/master/LICENSE)
[![Python Versions](https://img.shields.io/pypi/pyversions/lightgbm.svg)](https://pypi.org/project/lightgbm)
[![PyPI Version](https://img.shields.io/pypi/v/lightgbm.svg)](https://pypi.org/project/lightgbm)
[![Join the chat at https://gitter.im/Microsoft/LightGBM](https://badges.gitter.im/Microsoft/LightGBM.svg)](https://gitter.im/Microsoft/LightGBM?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[![Slack](https://lightgbm-slack-autojoin.herokuapp.com/badge.svg)](https://lightgbm-slack-autojoin.herokuapp.com)
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 and GPU learning.
- Capable of handling large-scale data.
For further details, please refer to [Features](https://github.com/Microsoft/LightGBM/blob/master/docs/Features.rst).
Benefitting from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/Microsoft/LightGBM/blob/master/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions.
[Comparison experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#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, [parallel experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
News
----
08/15/2017 : Optimal split for categorical features.
07/13/2017 : [Gitter](https://gitter.im/Microsoft/LightGBM) is available.
06/20/2017 : Python-package is on [PyPI](https://pypi.org/project/lightgbm) now.
06/09/2017 : [LightGBM Slack team](https://lightgbm.slack.com) is available.
05/03/2017 : LightGBM v2 stable release.
04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our [GPU Tutorial](./docs/GPU-Tutorial.rst) and [Performance Comparison](./docs/GPU-Performance.rst).
02/20/2017 : Update to LightGBM v2.
02/12/2017 : LightGBM v1 stable release.
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).
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.
More detailed update logs : [Key Events](https://github.com/Microsoft/LightGBM/blob/master/docs/Key-Events.md).
External (Unofficial) Repositories
----------------------------------
Julia-package: https://github.com/Allardvm/LightGBM.jl
JPMML: https://github.com/jpmml/jpmml-lightgbm
Get Started and Documentation
-----------------------------
Install by following [guide](https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst) for the command line program, [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/blob/master/docs/Quick-Start.rst) guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
Next you may want to read:
* [**Examples**](https://github.com/Microsoft/LightGBM/tree/master/examples) showing command line usage of common tasks.
* [**Features**](https://github.com/Microsoft/LightGBM/blob/master/docs/Features.rst) and algorithms supported by LightGBM.
* [**Parameters**](https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst) is an exhaustive list of customization you can make.
* [**Parallel Learning**](https://github.com/Microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/Microsoft/LightGBM/blob/master/docs/GPU-Tutorial.rst) can speed up computation.
* [**Laurae++ interactive documentation**](https://sites.google.com/view/lauraepp/parameters) is a detailed guide for hyperparameters.
Documentation for contributors:
* [**How we update readthedocs.io**](https://github.com/Microsoft/LightGBM/blob/master/docs/README.rst).
* Check out the [**Development Guide**](https://github.com/Microsoft/LightGBM/blob/master/docs/Development-Guide.rst).
Support
-------
* 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).
* Discuss on the [LightGBM Slack team](https://lightgbm.slack.com).
* Use [this invite link](https://lightgbm-slack-autojoin.herokuapp.com/) to join the team.
* Open **bug reports** and **feature requests** (not questions) on [GitHub issues](https://github.com/Microsoft/LightGBM/issues).
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.
- Add your stories and experience to [Awesome LightGBM](https://github.com/Microsoft/LightGBM/blob/master/examples/README.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.
Reference Papers
----------------
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. "[LightGBM: A Highly Efficient Gradient Boosting Decision Tree](https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree)". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-
没有合适的资源?快使用搜索试试~ 我知道了~
资源推荐
资源详情
资源评论
收起资源包目录
LightGBM-GPU 安装包已编译 (1192个子文件)
00Index 717B
make.bat 831B
feature_tests.bin 51KB
ompver_C.bin 49KB
ompver_CXX.bin 49KB
CMakeDetermineCompilerABI_CXX.bin 49KB
CMakeDetermineCompilerABI_C.bin 49KB
CMakeCCompilerId.c 19KB
OpenMPCheckVersion.c 626B
feature_tests.c 287B
R_init.c 261B
OpenMPTryFlag.c 132B
cmake.check_cache 86B
histogram16.cl 42KB
histogram256.cl 36KB
histogram64.cl 34KB
program.cl 553B
invalid_program.cl 513B
FindTBB.cmake 13KB
cmake_install.cmake 10KB
FindEigen.cmake 7KB
FindBolt.cmake 6KB
CMakeCXXCompiler.cmake 5KB
FindOpenCL.cmake 3KB
CMakeCCompiler.cmake 2KB
CMakeSystem.cmake 395B
CMakeRCCompiler.cmake 212B
train.conf 4KB
train.conf 3KB
train.conf 3KB
train.conf 3KB
train.conf 1KB
train.conf 93B
predict.conf 81B
predict.conf 79B
predict.conf 77B
predict.conf 77B
predict.conf 75B
predict.conf 73B
config 403B
config 333B
c_api.cpp 56KB
gpu_tree_learner.cpp 53KB
dataset_loader.cpp 46KB
test_copy_type_mismatch.cpp 42KB
serial_tree_learner.cpp 37KB
dataset.cpp 31KB
tree.cpp 28KB
gbdt.cpp 27KB
json11.cpp 25KB
voting_parallel_tree_learner.cpp 24KB
config_auto.cpp 21KB
test_lambda.cpp 20KB
gbdt_model_text.cpp 20KB
bin.cpp 20KB
CMakeCXXCompilerId.cpp 19KB
lightgbm_R.cpp 19KB
metadata.cpp 18KB
test_radix_sort.cpp 18KB
test_scan.cpp 17KB
test_vector.cpp 16KB
test_extrema.cpp 15KB
test_valarray.cpp 14KB
network.cpp 13KB
test_sort.cpp 13KB
matrix_transpose.cpp 13KB
test_copy.cpp 13KB
test_program.cpp 12KB
data_parallel_tree_learner.cpp 11KB
test_command_queue.cpp 11KB
config.cpp 11KB
test_merge_sort_gpu.cpp 11KB
opencv_optical_flow.cpp 10KB
test_kernel.cpp 10KB
application.cpp 10KB
test_radix_sort_by_key.cpp 10KB
test_transform.cpp 10KB
test_device.cpp 10KB
test_fill.cpp 10KB
opencv_sobel_filter.cpp 9KB
opencv_convolution.cpp 9KB
test_accumulate.cpp 8KB
test_reduce.cpp 8KB
k_means.cpp 8KB
opengl_sphere.cpp 8KB
opencv_histogram.cpp 8KB
test_reduce_by_key.cpp 8KB
resize_image.cpp 7KB
test_stable_sort_by_key.cpp 7KB
test_sort_by_key.cpp 7KB
test_insertion_sort.cpp 7KB
test_image2d.cpp 7KB
linkers_socket.cpp 7KB
test_discrete_distribution.cpp 7KB
nbody.cpp 7KB
test_zip_iterator.cpp 7KB
opencl_test.cpp 7KB
test_complex.cpp 7KB
test_pair.cpp 7KB
test_function.cpp 6KB
共 1192 条
- 1
- 2
- 3
- 4
- 5
- 6
- 12
资源评论
Cosolar
- 粉丝: 2
- 资源: 15
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功