{"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/layered-tpot-speeding-up-tree-based-pipeline","title":"Layered TPOT: Speeding up Tree-based Pipeline Optimization","arxiv_id":"1801.06007","date":"2018-01-18","proceeding":null,"authors":["Pieter Gijsbers","Joaquin Vanschoren","Randal S. Olson"],"abstract":"With the demand for machine learning increasing, so does the demand for tools\nwhich make it easier to use. Automated machine learning (AutoML) tools have\nbeen developed to address this need, such as the Tree-Based Pipeline\nOptimization Tool (TPOT) which uses genetic programming to build optimal\npipelines. We introduce Layered TPOT, a modification to TPOT which aims to\ncreate pipelines equally good as the original, but in significantly less time.\nThis approach evaluates candidate pipelines on increasingly large subsets of\nthe data according to their fitness, using a modified evolutionary algorithm to\nallow for separate competition between pipelines trained on different sample\nsizes. Empirical evaluation shows that, on sufficiently large datasets, Layered\nTPOT indeed finds better models faster.","url_abs":"http://arxiv.org/abs/1801.06007v2","url_pdf":"http://arxiv.org/pdf/1801.06007v2.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":"layered-tpot-speeding-up-tree-based-pipeline","repo_url":"https://github.com/EpistasisLab/tpot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"LGPL-3.0"}}],"tasks":[{"task_slug":"automl","task_name":"AutoML"},{"task_slug":"automated-feature-engineering","task_name":"Automated Feature Engineering"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1801.06007","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}