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some more changes to remove redundant information

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Ajinkya Kale 2015-07-25 12:46:28 -07:00
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README.md
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@ -28,17 +28,17 @@ What's New
---------- ----------
* XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance) * 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-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) * 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) * [External Memory Version](doc/external_memory.md)
Version Version
------- -------
* Current version xgboost-0.4, a lot improvment has been made since 0.3 * Current version xgboost-0.4
- Change log in [CHANGES.md](CHANGES.md) - [Change log](CHANGES.md)
- This version is compatible with 0.3x versions - This version is compatible with 0.3x versions
Features Features
@ -48,8 +48,7 @@ Features
* Fast and memory efficient * 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) - 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 - Handles sparse matrices, support external memory
* Accurate prediction, and used extensively by data scientists and kagglers * Accurate prediction, and used extensively by data scientists and kagglers - [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links)
- See [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links)
* Distributed and Portable * Distributed and Portable
- The distributed version runs on Hadoop (YARN), MPI, SGE etc. - The distributed version runs on Hadoop (YARN), MPI, SGE etc.
- Scales to billions of examples and beyond - Scales to billions of examples and beyond
@ -75,5 +74,5 @@ License
XGBoost in Graphlab Create 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 * 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 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 * 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