{"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/designing-and-building-the-mlpack-open-source","title":"Designing and building the mlpack open-source machine learning library","arxiv_id":"1708.05279","date":"2017-08-17","proceeding":null,"authors":["Ryan R. Curtin","Marcus Edel"],"abstract":"mlpack is an open-source C++ machine learning library with an emphasis on\nspeed and flexibility. Since its original inception in 2007, it has grown to be\na large project implementing a wide variety of machine learning algorithms,\nfrom standard techniques such as decision trees and logistic regression to\nmodern techniques such as deep neural networks as well as other\nrecently-published cutting-edge techniques not found in any other library.\nmlpack is quite fast, with benchmarks showing mlpack outperforming other\nlibraries' implementations of the same methods. mlpack has an active community,\nwith contributors from around the world---including some from PUST. This short\npaper describes the goals and design of mlpack, discusses how the open-source\ncommunity functions, and shows an example usage of mlpack for a simple data\nscience problem.","url_abs":"http://arxiv.org/abs/1708.05279v2","url_pdf":"http://arxiv.org/pdf/1708.05279v2.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":"designing-and-building-the-mlpack-open-source","repo_url":"https://github.com/mlpack/mlpack","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}