dmlc/xgboost

[](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/howtousecometwithxgboosttutorial.ipynb) Blog | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples. © Contributors, 2021. Licensed under an Apache-2 license. XGBoost…