{"data":{"slug":"dmlc-xgboost","name":"xgboost","tagline":"Scalable, Portable and Distributed Gradient Boosting Library","github_url":"https://github.com/dmlc/xgboost","owner":"dmlc","repo":"xgboost","owner_avatar_url":"https://avatars.githubusercontent.com/u/11508361?v=4","primary_language":"C++","stars":28796,"forks":8912,"topics":["distributed-systems","gbdt","gbm","gbrt","machine-learning","xgboost"],"archived":false,"github_pushed_at":"2026-09-26T10:15:19+00:00","maintenance_label":"Very active","stars_delta_30d":176,"url":"https://www.graphcanon.com/tools/dmlc-xgboost","markdown_url":"https://www.graphcanon.com/tools/dmlc-xgboost.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/dmlc-xgboost","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=dmlc-xgboost","description":"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","homepage_url":"https://xgboost.readthedocs.io/","license":"Apache-2.0","open_issues":442,"watchers":885,"ai_summary":"A highly efficient, flexible, and portable gradient boosting library supporting multiple languages including Python, R, Java, Scala, C++, running on various distributed environments.","readme_excerpt":"<img src=\"https://xgboost.ai/images/logo/xgboost-logo-trimmed.png\" width=200/> eXtreme Gradient Boosting\n===========\n\n\n\n\n\n\n\n\n\n\n\n\n[Blog](https://xgboost.ai/blog) |\n[Documentation](https://xgboost.readthedocs.io) |\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.\nCheck out the [contributor documentation](https://xgboost.readthedocs.io/en/latest/contrib/index.html).\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 \n\n### Sponsors\n[[Become a sponsor](https://opencollective.com/xgboost#sponsor)]\n\n<a href=\"https://www.nvidia.com/en-us/\" target=\"_blank\"><img src=\"https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/master/images/sponsors/nvidia.jpg\" alt=\"NVIDIA\" width=\"72\" height=\"72\"></a>\n<a href=\"https://www.comet.com/site/?utm_source=xgboost&utm_medium=github&utm_content=readme\" target=\"_blank\"><img src=\"https://cdn.comet.ml/img/notebook_logo.png\" height=\"72\"></a>\n<a href=\"https://opencollective.com/tomislav1\" target=\"_blank\"><img src=\"https://images.opencollective.com/tomislav1/avatar/256.png\" height=\"72\"></a>\n<a href=\"https://databento.com/?utm_source=xgboost&utm_medium=sponsor&utm_content=display\"><img src=\"https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/refs/heads/master/images/sponsors/databento.png\" height=\"72\"></a>\n<a href=\"https://www.intel.com/\" target=\"_blank\"><img src=\"https://images.opencollective.com/intel-corporation/2fa85c1/logo/256.png\" width=\"72\" height=\"72\"></a>\n\n### Backers\n[[Become a backer](https://opencollective.com/xgboost#backer)]\n\n<a href=\"https://opencollective.com/xgboost#backers\" target=\"_blank\"><img src=\"https://opencollective.com/xgboost/backers.svg?width=890\"></a>","github_created_at":"2014-02-06T17:28:03+00:00","created_at":"2026-07-11T23:24:49.579832+00:00","updated_at":"2026-09-27T06:00:15.476267+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"distributed-systems","name":"distributed-systems"},{"slug":"gbdt","name":"gbdt"},{"slug":"gbm","name":"gbm"},{"slug":"gbrt","name":"gbrt"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"xgboost","name":"xgboost"}],"trust":{"provenance":{"is_fork":false,"github_id":16587283,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-27T06:00:14.564Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":4,"days_since_push":0,"last_release_at":"2026-09-15T08:48:08Z","stars_delta_30d":176,"open_issues_delta_30d":26},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:24:51.187Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-27T06:00:15.180Z"},"languages":{"value":["c++"],"source":"github.language","observed_at":"2026-09-27T06:00:15.180Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-27T06:00:15.180Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.","Flexible with multi-language support (Python, R, Java) enhancing cross-platform applicability.","Ideal for distributed computing environments such as Hadoop, Spark enabling scalable machine learning."],"when_not_to_use":["Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability.","Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed.","Steer clear if your project does not require high-performance gradient boosting models for regression or classification."],"source":"enrich:decision_facts","observed_at":"2026-07-16T20:08:34.062Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license"},{"label":"License detail","value":"Apache-2.0 license allows free use, modification and distribution but requires preservation of copyright notices from source files and reproduction of license grants into any copyings of the codebase"}]}}