Merge pull request #408 from ajkl/patch-10
restructuring the README with an index
This commit is contained in:
Tianqi Chen
commit
195f90159d
101
README.md
101
README.md
@ -1,72 +1,77 @@
|
||||
DMLC/XGBoost
|
||||
==================================
|
||||
=======
|
||||
|
||||
[](https://travis-ci.org/dmlc/xgboost) [](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)
|
||||
Check out the [winning solution](doc/README.md#highlight-links)
|
||||
* XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04)
|
||||
* XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
|
||||
- Check out the winning solution at [Highlight links](doc/README.md#highlight-links)
|
||||
Check out the [winning solution](doc/README.md#highlight-links)
|
||||
* [External Memory Version](doc/external_memory.md)
|
||||
|
||||
Version
|
||||
-------
|
||||
|
||||
* Current version xgboost-0.4
|
||||
- [Change log](CHANGES.md)
|
||||
- This version is compatible with 0.3x versions
|
||||
|
||||
Features
|
||||
--------
|
||||
|
||||
* Easily accessible through CLI, [python](guide-python/basic_walkthrough.py),
|
||||
[R](../R-package/demo/basic_walkthrough.R),
|
||||
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)
|
||||
* Its fast! Benchmark numbers comparing xgboost, H20, Spark, R - [benchm-ml numbers](https://github.com/szilard/benchm-ml)
|
||||
* Memory efficient - Handles sparse matrices, supports external memory
|
||||
* Accurate prediction, and used extensively by data scientists and kagglers - [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links)
|
||||
* Distributed version runs on Hadoop (YARN), MPI, SGE etc., scales to billions of examples.
|
||||
|
||||
Bug Reporting
|
||||
-------------
|
||||
|
||||
* 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/)
|
||||
|
||||
|
||||
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.
|
||||
|
||||
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
|
||||
- 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
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
--------------------------
|
||||
* 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](http://graphlab.com/products/create/quick-start-guide.html)
|
||||
* Nice [blogpost](http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand) by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user