{"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/novel-prediction-techniques-based-on","title":"Novel Prediction Techniques Based on Clusterwise Linear Regression","arxiv_id":"1804.10742","date":"2018-04-28","proceeding":null,"authors":["Igor Gitman","Jieshi Chen","Eric Lei","Artur Dubrawski"],"abstract":"In this paper we explore different regression models based on Clusterwise\nLinear Regression (CLR). CLR aims to find the partition of the data into $k$\nclusters, such that linear regressions fitted to each of the clusters minimize\noverall mean squared error on the whole data. The main obstacle preventing to\nuse found regression models for prediction on the unseen test points is the\nabsence of a reasonable way to obtain CLR cluster labels when the values of\ntarget variable are unknown. In this paper we propose two novel approaches on\nhow to solve this problem. The first approach, predictive CLR builds a separate\nclassification model to predict test CLR labels. The second approach,\nconstrained CLR utilizes a set of user-specified constraints that enforce\ncertain points to go to the same clusters. Assuming the constraint values are\nknown for the test points, they can be directly used to assign CLR labels. We\nevaluate these two approaches on three UCI ML datasets as well as on a large\ncorpus of health insurance claims. We show that both of the proposed algorithms\nsignificantly improve over the known CLR-based regression methods. Moreover,\npredictive CLR consistently outperforms linear regression and random forest,\nand shows comparable performance to support vector regression on UCI ML\ndatasets. The constrained CLR approach achieves the best performance on the\nhealth insurance dataset, while enjoying only $\\approx 20$ times increased\ncomputational time over linear regression.","url_abs":"http://arxiv.org/abs/1804.10742v1","url_pdf":"http://arxiv.org/pdf/1804.10742v1.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":"novel-prediction-techniques-based-on","repo_url":"https://github.com/Kipok/clr_prediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"linear-regression","method_name":"Linear Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}