{"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/identifying-and-harnessing-the-building","title":"Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool","arxiv_id":"1607.08878","date":"2016-07-29","proceeding":null,"authors":["Randal S. Olson","Jason H. Moore"],"abstract":"As data science continues to grow in popularity, there will be an increasing\nneed to make data science tools more scalable, flexible, and accessible. In\nparticular, automated machine learning (AutoML) systems seek to automate the\nprocess of designing and optimizing machine learning pipelines. In this\nchapter, we present a genetic programming-based AutoML system called TPOT that\noptimizes a series of feature preprocessors and machine learning models with\nthe goal of maximizing classification accuracy on a supervised classification\nproblem. Further, we analyze a large database of pipelines that were previously\nused to solve various supervised classification problems and identify 100 short\nseries of machine learning operations that appear the most frequently, which we\ncall the building blocks of machine learning pipelines. We harness these\nbuilding blocks to initialize TPOT with promising solutions, and find that this\nsensible initialization method significantly improves TPOT's performance on one\nbenchmark at no cost of significantly degrading performance on the others.\nThus, sensible initialization with machine learning pipeline building blocks\nshows promise for GP-based AutoML systems, and should be further refined in\nfuture work.","url_abs":"http://arxiv.org/abs/1607.08878v1","url_pdf":"http://arxiv.org/pdf/1607.08878v1.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":"identifying-and-harnessing-the-building","repo_url":"https://github.com/rhiever/tpot","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"automl","task_name":"AutoML"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"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}