{"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/atmseer-increasing-transparency-and","title":"ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning","arxiv_id":"1902.05009","date":"2019-02-13","proceeding":null,"authors":["Qianwen Wang","Yao Ming","Zhihua Jin","Qiaomu Shen","Dongyu Liu","Micah J. Smith","Kalyan Veeramachaneni","Huamin Qu"],"abstract":"To relieve the pain of manually selecting machine learning algorithms and\ntuning hyperparameters, automated machine learning (AutoML) methods have been\ndeveloped to automatically search for good models. Due to the huge model search\nspace, it is impossible to try all models. Users tend to distrust automatic\nresults and increase the search budget as much as they can, thereby undermining\nthe efficiency of AutoML. To address these issues, we design and implement\nATMSeer, an interactive visualization tool that supports users in refining the\nsearch space of AutoML and analyzing the results. To guide the design of\nATMSeer, we derive a workflow of using AutoML based on interviews with machine\nlearning experts. A multi-granularity visualization is proposed to enable users\nto monitor the AutoML process, analyze the searched models, and refine the\nsearch space in real time. We demonstrate the utility and usability of ATMSeer\nthrough two case studies, expert interviews, and a user study with 13 end\nusers.","url_abs":"http://arxiv.org/abs/1902.05009v1","url_pdf":"http://arxiv.org/pdf/1902.05009v1.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":"atmseer-increasing-transparency-and","repo_url":"https://github.com/HDI-Project/ATMSeer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"automl","task_name":"AutoML"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.05009","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}