[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9567":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":32,"readmeContent":33,"aiSummary":34,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":35,"discoverSource":36},9567,"xgboost","dmlc\u002Fxgboost","dmlc","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","https:\u002F\u002Fxgboost.readthedocs.io\u002F",null,"C++",28464,8878,885,397,0,2,22,110,15,95,"Apache License 2.0",false,"master",true,[27,28,29,30,31,5],"distributed-systems","gbdt","gbm","gbrt","machine-learning","2026-06-12 04:00:45","\u003Cimg src=\"https:\u002F\u002Fxgboost.ai\u002Fimages\u002Flogo\u002Fxgboost-logo-trimmed.png\" width=200\u002F> eXtreme Gradient Boosting\n===========\n\n[![XGBoost-CI](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fxgboost\u002Fworkflows\u002FXGBoost%20CI\u002Fbadge.svg?branch=master)](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fxgboost\u002Factions)\n[![Documentation Status](https:\u002F\u002Freadthedocs.org\u002Fprojects\u002Fxgboost\u002Fbadge\u002F?version=latest)](https:\u002F\u002Fxgboost.readthedocs.org)\n[![GitHub license](https:\u002F\u002Fdmlc.github.io\u002Fimg\u002Fapache2.svg)](.\u002FLICENSE)\n[![CRAN Status Badge](https:\u002F\u002Fwww.r-pkg.org\u002Fbadges\u002Fversion\u002Fxgboost)](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Fxgboost)\n[![PyPI version](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fxgboost.svg)](https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Fxgboost\u002F)\n[![Conda version](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fvn\u002Fconda-forge\u002Fpy-xgboost.svg)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fpy-xgboost)\n[![Optuna](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FOptuna-integrated-blue)](https:\u002F\u002Foptuna.org)\n[![Twitter](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F@XGBoostProject--_.svg?style=social&logo=twitter)](https:\u002F\u002Ftwitter.com\u002FXGBoostProject)\n[![OpenSSF Scorecard](https:\u002F\u002Fapi.securityscorecards.dev\u002Fprojects\u002Fgithub.com\u002Fdmlc\u002Fxgboost\u002Fbadge)](https:\u002F\u002Fapi.securityscorecards.dev\u002Fprojects\u002Fgithub.com\u002Fdmlc\u002Fxgboost)\n[![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fcomet-ml\u002Fcomet-examples\u002Fblob\u002Fmaster\u002Fintegrations\u002Fmodel-training\u002Fxgboost\u002Fnotebooks\u002Fhow_to_use_comet_with_xgboost_tutorial.ipynb)\n\n[Community](https:\u002F\u002Fxgboost.ai\u002Fcommunity) |\n[Documentation](https:\u002F\u002Fxgboost.readthedocs.org) |\n[Resources](demo\u002FREADME.md) |\n[Contributors](CONTRIBUTORS.md) |\n[Release Notes](https:\u002F\u002Fxgboost.readthedocs.io\u002Fen\u002Flatest\u002Fchanges\u002Findex.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:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGradient_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:\u002F\u002Fgithub.com\u002Fdmlc\u002Fxgboost\u002Fblob\u002Fmaster\u002FLICENSE) 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:\u002F\u002Fxgboost.ai\u002Fcommunity).\n\nReference\n---------\n- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](https:\u002F\u002Farxiv.org\u002Fabs\u002F1603.02754). 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:\u002F\u002Fxgboost.ai\u002Fsponsors). The funds are used to defray the cost of continuous integration and testing infrastructure (https:\u002F\u002Fxgboost-ci.net).\n\n## Open Source Collective sponsors\n[![Backers on Open Collective](https:\u002F\u002Fopencollective.com\u002Fxgboost\u002Fbackers\u002Fbadge.svg)](#backers) [![Sponsors on Open Collective](https:\u002F\u002Fopencollective.com\u002Fxgboost\u002Fsponsors\u002Fbadge.svg)](#sponsors)\n\n### Sponsors\n[[Become a sponsor](https:\u002F\u002Fopencollective.com\u002Fxgboost#sponsor)]\n\n\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002F\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fxgboost-ai\u002Fxgboost-ai.github.io\u002Fmaster\u002Fimages\u002Fsponsors\u002Fnvidia.jpg\" alt=\"NVIDIA\" width=\"72\" height=\"72\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.comet.com\u002Fsite\u002F?utm_source=xgboost&utm_medium=github&utm_content=readme\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fcdn.comet.ml\u002Fimg\u002Fnotebook_logo.png\" height=\"72\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fopencollective.com\u002Ftomislav1\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fimages.opencollective.com\u002Ftomislav1\u002Favatar\u002F256.png\" height=\"72\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdatabento.com\u002F?utm_source=xgboost&utm_medium=sponsor&utm_content=display\">\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fxgboost-ai\u002Fxgboost-ai.github.io\u002Frefs\u002Fheads\u002Fmaster\u002Fimages\u002Fsponsors\u002Fdatabento.png\" height=\"72\">\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.intel.com\u002F\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fimages.opencollective.com\u002Fintel-corporation\u002F2fa85c1\u002Flogo\u002F256.png\" width=\"72\" height=\"72\">\u003C\u002Fa>\n\n### Backers\n[[Become a backer](https:\u002F\u002Fopencollective.com\u002Fxgboost#backer)]\n\n\u003Ca href=\"https:\u002F\u002Fopencollective.com\u002Fxgboost#backers\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fopencollective.com\u002Fxgboost\u002Fbackers.svg?width=890\">\u003C\u002Fa>\n","XGBoost是一个高效的分布式梯度提升库，用于实现机器学习中的梯度提升决策树（GBDT, GBM）算法。它支持多种编程语言如Python、R、Java等，并能在单机以及Hadoop、Spark等多种分布式环境中运行，处理大规模数据集。其核心功能包括并行计算能力、灵活的参数设置和强大的模型性能优化技术。XGBoost特别适用于需要高精度预测的大规模数据分析场景，比如金融风险评估、广告点击率预测等领域。","2026-06-11 03:23:25","top_topic"]