{"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/mondrian-forests-efficient-online-random","title":"Mondrian Forests: Efficient Online Random Forests","arxiv_id":"1406.2673","date":"2014-06-10","proceeding":"NeurIPS 2014 12","authors":["Balaji Lakshminarayanan","Daniel M. Roy","Yee Whye Teh"],"abstract":"Ensembles of randomized decision trees, usually referred to as random\nforests, are widely used for classification and regression tasks in machine\nlearning and statistics. Random forests achieve competitive predictive\nperformance and are computationally efficient to train and test, making them\nexcellent candidates for real-world prediction tasks. The most popular random\nforest variants (such as Breiman's random forest and extremely randomized\ntrees) operate on batches of training data. Online methods are now in greater\ndemand. Existing online random forests, however, require more training data\nthan their batch counterpart to achieve comparable predictive performance. In\nthis work, we use Mondrian processes (Roy and Teh, 2009) to construct ensembles\nof random decision trees we call Mondrian forests. Mondrian forests can be\ngrown in an incremental/online fashion and remarkably, the distribution of\nonline Mondrian forests is the same as that of batch Mondrian forests. Mondrian\nforests achieve competitive predictive performance comparable with existing\nonline random forests and periodically re-trained batch random forests, while\nbeing more than an order of magnitude faster, thus representing a better\ncomputation vs accuracy tradeoff.","url_abs":"http://arxiv.org/abs/1406.2673v2","url_pdf":"http://arxiv.org/pdf/1406.2673v2.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":"mondrian-forests-efficient-online-random","repo_url":"https://github.com/balajiln/mondrianforest","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"mondrian-forests-efficient-online-random","repo_url":"https://github.com/harveydevereux/Mondrian","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1406.2673","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}