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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 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.
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Contributors: https://github.com/tqchen/xgboost/graphs/contributors
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Contributors: https://github.com/dmlc/xgboost/graphs/contributors
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Turorial and Documentation: https://github.com/tqchen/xgboost/wiki
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Turorial and Documentation: https://github.com/dmlc/xgboost/wiki
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Issues Tracker: [https://github.com/tqchen/xgboost/issues](https://github.com/tqchen/xgboost/issues?q=is%3Aissue+label%3Aquestion)
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Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion)
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Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask questions and share your experience on xgboost.
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