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xgboost: eXtreme Gradient Boosting XGBoost: eXtreme Gradient Boosting
====== ==================================
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 algorithm under gradient boosting framework, including generalized linear model and gradient boosted regression tree (GBDT). XGBoost can also also distributed and scale to even larger data. It implements machine learning algorithm under gradient boosting framework, including generalized linear model and gradient boosted regression tree (GBDT). XGBoost can also also distributed and scale to even larger data.
@ -23,7 +23,7 @@ Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washin
* The model presented is used by xgboost for boosted trees * The model presented is used by xgboost for boosted trees
What's New What's New
===== ==========
* [Distributed XGBoost now runs on YARN](multi-node/hadoop)! * [Distributed XGBoost now runs on YARN](multi-node/hadoop)!
* [xgboost user group](https://groups.google.com/forum/#!forum/xgboost-user/) for tracking changes, sharing your experience on xgboost * [xgboost user group](https://groups.google.com/forum/#!forum/xgboost-user/) for tracking changes, sharing your experience on xgboost
* [Distributed XGBoost](multi-node) is now available!! * [Distributed XGBoost](multi-node) is now available!!
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* Thanks to Tong He, the new [R package](R-package) is available * Thanks to Tong He, the new [R package](R-package) is available
Features Features
====== ========
* Sparse feature format: * Sparse feature format:
- Sparse feature format allows easy handling of missing values, and improve computation efficiency. - Sparse feature format allows easy handling of missing values, and improve computation efficiency.
* Push the limit on single machine: * Push the limit on single machine:
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Then run ```bash build.sh``` normally. Then run ```bash build.sh``` normally.
Version Version
====== =======
* This version xgboost-0.3, the code has been refactored from 0.2x to be cleaner and more flexibility * This version xgboost-0.3, the code has been refactored from 0.2x to be cleaner and more flexibility
* This version of xgboost is not compatible with 0.2x, due to huge amount of changes in code structure * This version of xgboost is not compatible with 0.2x, due to huge amount of changes in code structure
- This means the model and buffer file of previous version can not be loaded in xgboost-3.0 - This means the model and buffer file of previous version can not be loaded in xgboost-3.0
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* Change log in [CHANGES.md](CHANGES.md) * Change log in [CHANGES.md](CHANGES.md)
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 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 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 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 using GLC boosted tree to solve kaggle bike sharing challenge: http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand