{"id":16947030,"url":"https://github.com/mainro/xgbtune","last_synced_at":"2026-04-02T17:25:36.926Z","repository":{"id":62589898,"uuid":"237320095","full_name":"MainRo/xgbtune","owner":"MainRo","description":"a library to tune xgboost models","archived":false,"fork":false,"pushed_at":"2020-02-14T14:47:28.000Z","size":76,"stargazers_count":8,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-11T19:52:31.844Z","etag":null,"topics":["automl","automl-algorithms","gradient-boosting","hyperparameter-optimization","hyperparameter-tuning","machine-learning","parameter-tuning","tuning-parameters","xgboost"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MainRo.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-01-30T22:41:19.000Z","updated_at":"2023-12-11T07:40:52.000Z","dependencies_parsed_at":"2022-11-04T08:08:30.818Z","dependency_job_id":null,"html_url":"https://github.com/MainRo/xgbtune","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"purl":"pkg:github/MainRo/xgbtune","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MainRo%2Fxgbtune","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MainRo%2Fxgbtune/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MainRo%2Fxgbtune/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MainRo%2Fxgbtune/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MainRo","download_url":"https://codeload.github.com/MainRo/xgbtune/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MainRo%2Fxgbtune/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269827266,"owners_count":24481494,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-11T02:00:10.019Z","response_time":75,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["automl","automl-algorithms","gradient-boosting","hyperparameter-optimization","hyperparameter-tuning","machine-learning","parameter-tuning","tuning-parameters","xgboost"],"created_at":"2024-10-13T21:45:37.157Z","updated_at":"2026-04-02T17:25:36.886Z","avatar_url":"https://github.com/MainRo.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"==========\nXGBTune\n==========\n\n\n.. image:: https://badge.fury.io/py/xgbtune.svg\n    :target: https://badge.fury.io/py/xgbtune\n\n.. image:: https://github.com/mainro/xgbtune/workflows/Python%20package/badge.svg\n    :target: https://github.com/mainro/xgbtune/actions?query=workflow%3A%22Python+package%22\n    :alt: Github WorkFlows\n\n.. image:: https://readthedocs.org/projects/xgbtune/badge/?version=latest\n    :target: https://xgbtune.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n\n\nXGBTune is a library for automated XGBoost model tuning. Tuning an XGBoost\nmodel is as simple as a single function call.\n\nGet Started\n============\n\n.. code:: python\n\n    from xgbtune import tune_xgb_model\n\n    params, round_count = tune_xgb_model(params, x_train, y_train)\n\n\nInstall\n========\n\nXGBTune is available on PyPi and can be installed with pip:\n\n.. code:: console\n\n    pip install xgbtune\n\n\nTuning steps\n=============\n\nThe tuning is done in the following steps:\n\n* compute best round\n* tune max_depth and min_child_weight\n* tune gamma\n* re-compute best round\n* tune subsample and colsample_bytree\n* fine tune subsample and colsample_bytree\n* tune alpha and lambda\n* tune seed\n\nThis steps can be repeated several times. By default, two passes are done.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmainro%2Fxgbtune","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmainro%2Fxgbtune","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmainro%2Fxgbtune/lists"}