{"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/evaluation-of-a-tree-based-pipeline","title":"Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science","arxiv_id":"1603.06212","date":"2016-03-20","proceeding":null,"authors":["Randal S. Olson","Nathan Bartley","Ryan J. Urbanowicz","Jason H. Moore"],"abstract":"As the field of data science continues to grow, there will be an\never-increasing demand for tools that make machine learning accessible to\nnon-experts. In this paper, we introduce the concept of tree-based pipeline\noptimization for automating one of the most tedious parts of machine\nlearning---pipeline design. We implement an open source Tree-based Pipeline\nOptimization Tool (TPOT) in Python and demonstrate its effectiveness on a\nseries of simulated and real-world benchmark data sets. In particular, we show\nthat TPOT can design machine learning pipelines that provide a significant\nimprovement over a basic machine learning analysis while requiring little to no\ninput nor prior knowledge from the user. We also address the tendency for TPOT\nto design overly complex pipelines by integrating Pareto optimization, which\nproduces compact pipelines without sacrificing classification accuracy. As\nsuch, this work represents an important step toward fully automating machine\nlearning pipeline design.","url_abs":"http://arxiv.org/abs/1603.06212v1","url_pdf":"http://arxiv.org/pdf/1603.06212v1.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":"evaluation-of-a-tree-based-pipeline","repo_url":"https://github.com/rhiever/tpot","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"evaluation-of-a-tree-based-pipeline","repo_url":"https://github.com/DataCanvasIO/Hypernets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"evaluation-of-a-tree-based-pipeline","repo_url":"https://github.com/jim-schwoebel/allie","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"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"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}