{"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/dont-rule-out-simple-models-prematurely-a","title":"Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML","arxiv_id":null,"date":"2018-10-05","proceeding":"IDA 2018: Advances in Intelligent Data Analysis XVII 2018 10","authors":["Benjamin Strang","Peter van der Putten","Jan N. van Rijn","Frank Hutter"],"abstract":"A basic step for each data-mining or machine learning task is to determine which model to choose based on the problem and the data at hand. In this paper we investigate when non-linear classifiers outperform linear classifiers by means of a large scale experiment. We benchmark linear and non-linear versions of three types of classifiers (support vector machines; neural networks; and decision trees), and analyze the results to determine on what type of datasets the non-linear version performs better. To the best of our knowledge, this work is the first principled and large scale attempt to support the common assumption that non-linear classifiers excel only when large amounts of data are available.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-030-01768-2_25","url_pdf":"http://liacs.leidenuniv.nl/~rijnjnvan/pdf/pub/ida2018.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":"dont-rule-out-simple-models-prematurely-a","repo_url":"https://github.com/openml/openml-python/blob/develop/examples/40_paper/2018_ida_strang_example.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}