[LOG] Simplfy README.md add change logs.
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CHANGES.md
25
CHANGES.md
@ -35,6 +35,7 @@ xgboost-0.4
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* sklearn wrapper is supported in python module
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* Experimental External memory version
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xgboost-0.47
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------------
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* Changes in R library
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@ -52,10 +53,28 @@ xgboost-0.47
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- improved compatibility in sklearn module.
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- additional parameters added for sklearn wrapper.
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- added pip installation functionality.
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- supports more Pandas DataFrame dtypes.
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- supports more Pandas DataFrame dtypes.
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- added best_ntree_limit attribute, in addition to best_score and best_iteration.
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* Java api is ready for use
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* Added more test cases and continuous integration to make each build more robust.
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on going at master
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------------------
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xgboost brick: next release candidate
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-------------------------------------
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* Major refactor of core library.
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- Goal: more flexible and modular code as a portable library.
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- Switch to use of c++11 standard code.
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- Random number generator defaults to ```std::mt19937```.
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- Share the data loading pipeline and logging module from dmlc-core.
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- Enable registry pattern to allow optionally plugin of objective, metric, tree constructor, data loader.
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- Future plugin modules can be put into xgboost/plugin and register back to the library.
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- Remove most of the raw pointers to smart ptrs, for RAII safety.
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* Change library name to libxgboost.so
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* Backward compatiblity
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- The binary buffer file is not backward compatible with previous version.
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- The model file is backward compatible on 64 bit platforms.
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* The model file is compatible between 64/32 bit platforms(not yet tested).
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* External memory version and other advanced features will be exposed to R library as well on linux.
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- Previously some of the features are blocked due to C++11 and threading limits.
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- The windows version is still blocked due to Rtools do not support ```std::thread```.
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* rabit and dmlc-core are maintained through git submodule
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- Anyone can open PR to update these dependencies now.
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39
README.md
39
README.md
@ -7,47 +7,31 @@
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[](https://pypi.python.org/pypi/xgboost/)
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[](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.
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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
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XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/) projects
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XGBoost is an optimized distributed gradient boosting library designed to be highly *efficient*, *flexible* and *portable*.
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It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework.
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XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
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The same code runs on major distributed environment(Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
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XGBoost is part of [DMLC](http://dmlc.github.io/) projects.
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Contents
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--------
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* [What's New](#whats-new)
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* [Version](#version)
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* [Documentation](doc/index.md)
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* [Build Instruction](doc/build.md)
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* [Features](#features)
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* [Distributed XGBoost](multi-node)
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* [Documentation](https://xgboost.readthedocs.org)
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* [Usecases](doc/index.md#highlight-links)
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* [Bug Reporting](#bug-reporting)
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* [Contributing to XGBoost](#contributing-to-xgboost)
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* [Code Examples](demo)
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* [Build Instruction](doc/build.md)
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* [Committers and Contributors](CONTRIBUTORS.md)
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* [License](#license)
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* [XGBoost in Graphlab Create](#xgboost-in-graphlab-create)
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What's New
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----------
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* XGBoost [brick](CHANGES.md)
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* XGBoost helps Vlad Mironov, Alexander Guschin to win the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/11/30/flavour-of-physics-technical-write-up-1st-place-go-polar-bears/).
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* XGBoost helps Mario Filho, Josef Feigl, Lucas, Gilberto to win the [Caterpillar Tube Pricing competition](https://www.kaggle.com/c/caterpillar-tube-pricing). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/09/22/caterpillar-winners-interview-1st-place-gilberto-josef-leustagos-mario/).
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* XGBoost helps Halla Yang to win the [Recruit Coupon Purchase Prediction Challenge](https://www.kaggle.com/c/coupon-purchase-prediction). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/10/21/recruit-coupon-purchase-winners-interview-2nd-place-halla-yang/).
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* XGBoost helps Owen Zhang to win the [Avito Context Ad Click competition](https://www.kaggle.com/c/avito-context-ad-clicks). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/).
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* XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance)
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Check out the [winning solution](https://github.com/ChenglongChen/Kaggle_CrowdFlower)
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* XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04)
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* 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)
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Check out the [winning solution](doc/README.md#highlight-links)
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* [External Memory Version](doc/external_memory.md)
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Version
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-------
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* Current version xgboost-0.4
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- [Change log](CHANGES.md)
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- This version is compatible with 0.3x versions
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* Current version xgboost-0.6 (brick)
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- See [Change log](CHANGES.md) for details
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Features
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--------
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@ -76,4 +60,3 @@ XGBoost has been developed and used by a group of active community members. Ever
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License
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-------
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© Contributors, 2015. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.
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