{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-gradient-boosting","title":"Automatic Gradient Boosting","arxiv_id":"1807.03873","date":"2018-07-10","proceeding":null,"authors":["Janek Thomas","Stefan Coors","Bernd Bischl"],"abstract":"Automatic machine learning performs predictive modeling with high performing\nmachine learning tools without human interference. This is achieved by making\nmachine learning applications parameter-free, i.e. only a dataset is provided\nwhile the complete model selection and model building process is handled\ninternally through (often meta) optimization. Projects like Auto-WEKA and\nauto-sklearn aim to solve the Combined Algorithm Selection and Hyperparameter\noptimization (CASH) problem resulting in huge configuration spaces. However,\nfor most real-world applications, the optimization over only a few different\nkey learning algorithms can not only be sufficient, but also potentially\nbeneficial. The latter becomes apparent when one considers that models have to\nbe validated, explained, deployed and maintained. Here, less complex model are\noften preferred, for validation or efficiency reasons, or even a strict\nrequirement. Automatic gradient boosting simplifies this idea one step further,\nusing only gradient boosting as a single learning algorithm in combination with\nmodel-based hyperparameter tuning, threshold optimization and encoding of\ncategorical features. We introduce this general framework as well as a concrete\nimplementation called autoxgboost. It is compared to current AutoML projects on\n16 datasets and despite its simplicity is able to achieve comparable results on\nabout half of the datasets as well as performing best on two.","url_abs":"http://arxiv.org/abs/1807.03873v2","url_pdf":"http://arxiv.org/pdf/1807.03873v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatic-gradient-boosting","repo_url":"https://github.com/ja-thomas/autoxgboost","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"automatic-gradient-boosting","repo_url":"https://github.com/ja-thomas/autoxgboostMC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"automatic-gradient-boosting","repo_url":"https://github.com/pfistfl/autoxgboostMC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"automl","task_name":"AutoML"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"},{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}