diff --git a/README.md b/README.md index 4fabb7362..7a4cfa4c8 100644 --- a/README.md +++ b/README.md @@ -1,29 +1,32 @@ -DMLC/XGBoost -================================== +XGBoost +======= [![Build Status](https://travis-ci.org/dmlc/xgboost.svg?branch=master)](https://travis-ci.org/dmlc/xgboost) [![Gitter chat for developers at https://gitter.im/dmlc/xgboost](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) -An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. +An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. + It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data -Check out our [Committers and Contributors](CONTRIBUTORS.md) who help make xgboost better. - -Documentation: [Documentation of dmlc/xgboost](doc/README.md) - -Issue Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion) - -Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask questions and share your experience on xgboost. - - Use issue tracker for bug reports, feature requests etc. - - Use the user group to post your experience, ask questions about general usages. - -Distributed Version: [Distributed XGBoost](multi-node) - -Highlights of Usecases: [Highlight Links](doc/README.md#highlight-links) - XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/) projects +Contents +-------- +* [What's New](#whats-new) +* [Version](#version) +* [Documentation](doc/README.md) +* [Build Instruction](doc/build.md) +* [Features](#features) +* [Distributed XGBoost](multi-node) +* [Usecases](doc/README.md#highlight-links) +* [Bug Reporting](#bug-reporting) +* [Contributing to XGBoost](#contributing-to-xgboost) +* [Committers and Contributors](CONTRIBUTORS.md) +* [License](#license) +* [XGBoost in Graphlab Create](#xgboost-in-graphlab-create) + What's New -========== +---------- + * XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance) - Check out the winning solution at [Highlight links](doc/README.md#highlight-links) * XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04) @@ -31,42 +34,46 @@ What's New - Check out the winning solution at [Highlight links](doc/README.md#highlight-links) * [External Memory Version](doc/external_memory.md) -Contributing to XGBoost -========= -XGBoost has been developed and used by a group of active community members. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users. -* Check out [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something. -* Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users. -* Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) after your patch has been merged. +Version +------- + +* Current version xgboost-0.4, a lot improvment has been made since 0.3 + - Change log in [CHANGES.md](CHANGES.md) + - This version is compatible with 0.3x versions Features -======== -* Easily accessible in python, R, Julia, CLI -* Fast speed and memory efficient - - Can be more than 10 times faster than GBM in sklearn and R +-------- + +* Easily accessible through python, R, Julia, CLI +* Fast and memory efficient + - Can be more than 10 times faster than GBM in sklearn and R. [benchm-ml numbers](https://github.com/szilard/benchm-ml) - Handles sparse matrices, support external memory * Accurate prediction, and used extensively by data scientists and kagglers - See [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links) * Distributed and Portable - The distributed version runs on Hadoop (YARN), MPI, SGE etc. - Scales to billions of examples and beyond + +Bug Reporting +------------- -Build -======= -* Run ```bash build.sh``` (you can also type make) - - Normally it gives what you want - - See [Build Instruction](doc/build.md) for more information +* For reporting bugs please use the [xgboost/issues](https://github.com/dmlc/xgboost/issues) page. +* For generic questions or to share your experience using xgboost please use the [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) -Version -======= -* Current version xgboost-0.4, a lot improvment has been made since 0.3 - - Change log in [CHANGES.md](CHANGES.md) - - This version is compatible with 0.3x versions + +Contributing to XGBoost +----------------------- + +XGBoost has been developed and used by a group of active community members. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users. +* Check out [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something. +* Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users. +* Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) after your patch has been merged. License -======= +------- © Contributors, 2015. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license. XGBoost in Graphlab Create -========================== +-------------------------- * XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to do data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the Graphlab Create in http://graphlab.com/products/create/quick-start-guide.html * Nice blogpost by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge: http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand