restructuring the README with an index

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
+Version
-=========
+-------
-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.
+* Current version xgboost-0.4, a lot improvment has been made since 0.3
-* Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users.
+  - Change log in [CHANGES.md](CHANGES.md)
-* Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) after your patch has been merged.
+  - This version is compatible with 0.3x versions
 
 Features
-========
+--------
-* Easily accessible in python, R, Julia, CLI
+
-* Fast speed and memory efficient
+* Easily accessible through python, R, Julia, CLI
-  - Can be more than 10 times faster than GBM in sklearn and R
+* 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
+* 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/)
-* Run ```bash build.sh``` (you can also type make)
-  - Normally it gives what you want
-  - See [Build Instruction](doc/build.md) for more information
 
-Version
+
-=======
+Contributing to XGBoost
-* 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
+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