<commit_before>XGBoost: eXtreme Gradient Boosting 
==================================
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.

Contributors: https://github.com/dmlc/xgboost/graphs/contributors

Documentations: [Documentation of xgboost](doc/README.md)

Issues 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.

Distributed Version: [Distributed XGBoost](multi-node)


What's New
==========
* [External Memory Version](doc/external_memory.md)
* XGBoost wins [WWW2015  Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
* XGBoost now support HDFS and S3
* [Distributed XGBoost now runs on YARN](https://github.com/dmlc/wormhole/tree/master/learn/xgboost)
* [xgboost user group](https://groups.google.com/forum/#!forum/xgboost-user/) for tracking changes, sharing your experience on xgboost
* New features in the lastest changes :)
  - Distributed version that scale xgboost to even larger problems with cluster
  - Feature importance visualization in R module, thanks to <NAME>
  - Predict leaf index, see [demo/guide-python/predict_leaf_indices.py](demo/guide-python/predict_leaf_indices.py)  
* XGBoost wins [Tradeshift Text Classification](https://kaggle2.blob.core.windows.net/forum-message-attachments/60041/1813/TradeshiftTextClassification.pdf?sv=2012-02-12&se=2015-01-02T13%3A55%3A16Z&sr=b&sp=r&sig=5MHvyjCLESLexYcvbSRFumGQXCS7MVmfdBIY3y01tMk%3D)
* XGBoost wins [HEP meets ML Award in Higgs Boson Challenge](http://atlas.ch/news/2014/machine-learning-wins-the-higgs-challenge.html)

Features
========
* Sparse feature format:
  - Sparse feature format allows easy handling of missing values, and improve computation efficiency.
* Push the limit on single machine:
  - Efficient implementation that optimizes memory and computation.
* Speed: XGBoost is very fast
  - IN [demo/higgs/speedtest.py](demo/kaggle-higgs/speedtest.py), kaggle higgs data it is faster(on our machine 20 times faster using 4 threads) than sklearn.ensemble.GradientBoostingClassifier
* Layout of gradient boosting algorithm to support user defined objective<commit_msg>add highlights<commit_after>XGBoost: eXtreme Gradient Boosting 
==================================
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.

Contributors: https://github.com/dmlc/xgboost/graphs/contributors

Documentations: [Documentation of xgboost](doc/README.md)

Issues 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.

Distributed Version: [Distributed XGBoost](multi-node)

Highlights of Usecases: [Highlight Links](doc/README.md#highlight-links)

What's New
==========
* [External Memory Version](doc/external_memory.md)
* XGBoost wins [WWW2015  Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
* XGBoost now support HDFS and S3
* [Distributed XGBoost now runs on YARN](https://github.com/dmlc/wormhole/tree/master/learn/xgboost)
* [xgboost user group](https://groups.google.com/forum/#!forum/xgboost-user/) for tracking changes, sharing your experience on xgboost
* New features in the lastest changes :)
  - Distributed version that scale xgboost to even larger problems with cluster
  - Feature importance visualization in R module, thanks to <NAME>
  - Predict leaf index, see [demo/guide-python/predict_leaf_indices.py](demo/guide-python/predict_leaf_indices.py)  
* XGBoost wins [Tradeshift Text Classification](https://kaggle2.blob.core.windows.net/forum-message-attachments/60041/1813/TradeshiftTextClassification.pdf?sv=2012-02-12&se=2015-01-02T13%3A55%3A16Z&sr=b&sp=r&sig=5MHvyjCLESLexYcvbSRFumGQXCS7MVmfdBIY3y01tMk%3D)
* XGBoost wins [HEP meets ML Award in Higgs Boson Challenge](http://atlas.ch/news/2014/machine-learning-wins-the-higgs-challenge.html)

Features
========
* Sparse feature format:
  - Sparse feature format allows easy handling of missing values, and improve computation efficiency.
* Push the limit on single machine:
  - Efficient implementation that optimizes memory and computation.
* Speed: XGBoost is very fast
  - IN [demo/higgs/speedtest.py](demo/kaggle-higgs/speedtest.py), kaggle higgs data it is faster(on our machine 20 times faster using 4 threads) than sklearn.ensemble.GradientBoostingClassifier
* Layout of gradient boosting algorithm to support user defined objective
{'fraction_non_alphanumeric': 0.11104992657856094, 'fraction_numerical': 0.023127753303964757, 'mean_word_length': 5.79426433915212, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 14, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '29872', 'n_tokens_mistral': 1794, 'n_tokens_neox': 1627, 'n_words': 455}