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dmlc/xgboost

Repository: dmlc/xgboost Stars: 28701 Forks: 8889 Primary language: C++ Discovery sources: awesome:awesome-data-science, awesome:awesome-python Selection score: 83.26 Usefulness score: 9.7 Source confidence score: 5.1 Languages: C++, Python, Cuda, R, Scala, Java Topics: distributed-systems, gbdt, gbm, gbrt, machine-learning, xgboost Summary: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow README excerpt: <img src="https://xgboost.ai/images/logo/xgboost-logo-trimmed.png" width=200/> eXtreme Gradient Boosting =========== [](https://github.com/dmlc/xgboost/actions) [](https://xgboost.readthedocs.io) [](./LICENSE) [](https://cran.r-project.org/web/packages/xgboost) [](https://pypi.python.org/pypi/xgboost/) [](https://anaconda.org/conda-forge/py-xgboost) [](https://optuna.org) [](https://twitter.com/XGBoostProject) [](https://api.securityscorecards.dev/projects/github.com/dmlc/xgboost) [](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/xgboost/notebooks/how_to_use_comet_with_xgboost_tutorial.ipynb) [Blog](https://xgboost.ai/blog) | [Documentation](https://xgboost.readthedocs.io) | [Resources](demo/README.md) | [Contributors](CONTRIBUTORS.md) | [Release Notes](https://xgboost.readthedoc

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