{"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/machine-learning-automation-toolbox-mlaut","title":"Machine Learning Automation Toolbox (MLaut)","arxiv_id":"1901.03678","date":"2019-01-11","proceeding":null,"authors":["Viktor Kazakov","Franz J. Király"],"abstract":"In this paper we present MLaut (Machine Learning AUtomation Toolbox) for the\npython data science ecosystem. MLaut automates large-scale evaluation and\nbenchmarking of machine learning algorithms on a large number of datasets.\nMLaut provides a high-level workflow interface to machine algorithm algorithms,\nimplements a local back-end to a database of dataset collections, trained\nalgorithms, and experimental results, and provides easy-to-use interfaces to\nthe scikit-learn and keras modelling libraries. Experiments are easy to set up\nwith default settings in a few lines of code, while remaining fully\ncustomizable to the level of hyper-parameter tuning, pipeline composition, or\ndeep learning architecture.\n  As a principal test case for MLaut, we conducted a large-scale supervised\nclassification study in order to benchmark the performance of a number of\nmachine learning algorithms - to our knowledge also the first larger-scale\nstudy on standard supervised learning data sets to include deep learning\nalgorithms. While corroborating a number of previous findings in literature, we\nfound (within the limitations of our study) that deep neural networks do not\nperform well on basic supervised learning, i.e., outside the more specialized,\nimage-, audio-, or text-based tasks.","url_abs":"http://arxiv.org/abs/1901.03678v1","url_pdf":"http://arxiv.org/pdf/1901.03678v1.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":"machine-learning-automation-toolbox-mlaut","repo_url":"https://github.com/alan-turing-institute/mlaut","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"benchmarking","task_name":"Benchmarking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}