{"id":13423846,"url":"https://github.com/dmlc/xgboost","last_synced_at":"2025-05-12T17:44:43.930Z","repository":{"id":13889025,"uuid":"16587283","full_name":"dmlc/xgboost","owner":"dmlc","description":"Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library,  for Python, R, Java, Scala, C++ and more. 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src=\"https://xgboost.ai/images/logo/xgboost-logo-trimmed.png\" width=200/\u003e eXtreme Gradient Boosting\n===========\n\n[![Build Status](https://badge.buildkite.com/aca47f40a32735c00a8550540c5eeff6a4c1d246a580cae9b0.svg?branch=master)](https://buildkite.com/xgboost/xgboost-ci)\n[![XGBoost-CI](https://github.com/dmlc/xgboost/workflows/XGBoost-CI/badge.svg?branch=master)](https://github.com/dmlc/xgboost/actions)\n[![Documentation Status](https://readthedocs.org/projects/xgboost/badge/?version=latest)](https://xgboost.readthedocs.org)\n[![GitHub license](https://dmlc.github.io/img/apache2.svg)](./LICENSE)\n[![CRAN Status Badge](https://www.r-pkg.org/badges/version/xgboost)](https://cran.r-project.org/web/packages/xgboost)\n[![PyPI version](https://badge.fury.io/py/xgboost.svg)](https://pypi.python.org/pypi/xgboost/)\n[![Conda version](https://img.shields.io/conda/vn/conda-forge/py-xgboost.svg)](https://anaconda.org/conda-forge/py-xgboost)\n[![Optuna](https://img.shields.io/badge/Optuna-integrated-blue)](https://optuna.org)\n[![Twitter](https://img.shields.io/badge/@XGBoostProject--_.svg?style=social\u0026logo=twitter)](https://twitter.com/XGBoostProject)\n[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/dmlc/xgboost/badge)](https://api.securityscorecards.dev/projects/github.com/dmlc/xgboost)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)\n\n[Community](https://xgboost.ai/community) |\n[Documentation](https://xgboost.readthedocs.org) |\n[Resources](demo/README.md) |\n[Contributors](CONTRIBUTORS.md) |\n[Release Notes](https://xgboost.readthedocs.io/en/latest/changes/index.html)\n\nXGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***.\nIt implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework.\nXGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.\nThe same code runs on major distributed environment (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples.\n\nLicense\n-------\n© Contributors, 2021. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.\n\nContribute to XGBoost\n---------------------\nXGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.\nCheckout the [Community Page](https://xgboost.ai/community).\n\nReference\n---------\n- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](https://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016\n- XGBoost originates from research project at University of Washington.\n\nSponsors\n--------\nBecome a sponsor and get a logo here. See details at [Sponsoring the XGBoost Project](https://xgboost.ai/sponsors). The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).\n\n## Open Source Collective sponsors\n[![Backers on Open Collective](https://opencollective.com/xgboost/backers/badge.svg)](#backers) [![Sponsors on Open Collective](https://opencollective.com/xgboost/sponsors/badge.svg)](#sponsors)\n\n### Sponsors\n[[Become a sponsor](https://opencollective.com/xgboost#sponsor)]\n\n\u003ca href=\"https://www.nvidia.com/en-us/\" target=\"_blank\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/master/images/sponsors/nvidia.jpg\" alt=\"NVIDIA\" width=\"72\" height=\"72\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.comet.com/site/?utm_source=xgboost\u0026utm_medium=github\u0026utm_content=readme\" target=\"_blank\"\u003e\u003cimg src=\"https://cdn.comet.ml/img/notebook_logo.png\" height=\"72\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opencollective.com/guest-f5ebfc79\" target=\"_blank\"\u003e\u003cimg src=\"https://images.opencollective.com/guest-f5ebfc79/avatar/256.png\" height=\"72\"\u003e\u003c/a\u003e\n\n### Backers\n[[Become a backer](https://opencollective.com/xgboost#backer)]\n\n\u003ca href=\"https://opencollective.com/xgboost#backers\" target=\"_blank\"\u003e\u003cimg src=\"https://opencollective.com/xgboost/backers.svg?width=890\"\u003e\u003c/a\u003e\n","funding_links":["https://opencollective.com/xgboost","https://xgboost.ai/sponsors","https://opencollective.com/guest-f5ebfc79"],"categories":["C++","Machine Learning","资源列表","Training","梯度提升和树模型","Frameworks-for-Training","Frameworks for Training","Machine Learning Framework","Table of Contents","机器学习","Real-World Projects","Recently Updated","The Data Science Toolbox","进程间通信","Libraries","machine-learning","Computation and Communication Optimisation","机器学习框架","Implementations","Machine Learning [🔝](#readme)","2016","Core ML","📚 فهرست","List of Most Starred Github Projects related to Deep Learning","人工智能","Library","Awesome Python","Related Resources","Uncategorized","🤖 Machine Learning \u0026 AI","General tools"],"sub_categories":["机器学习","Monitoring","Gradient Boosting","Frameworks","Frameworks for Training","Popular-LLM","General Purpose Framework","AI / Machine Learning","[Oct 18, 2024](/content/2024/10/18/README.md)","General Machine Learning Packages","Tools","[Tools](#tools-1)","Speech Recognition","Machine Learning","Automatic Plotting","XGBoost","Models :rocket:","یادگیری ماشین","Machine learning","3. 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