{"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/kernel-based-ensemble-learning-in-python","title":"Kernel-Based Ensemble Learning in Python","arxiv_id":"1912.08311","date":"2019-12-17","proceeding":null,"authors":["Benjamin Guedj","Bhargav Srinivasa Desikan"],"abstract":"We propose a new supervised learning algorithm, for classification and regression problems where two or more preliminary predictors are available. We introduce \\texttt{KernelCobra}, a non-linear learning strategy for combining an arbitrary number of initial predictors. \\texttt{KernelCobra} builds on the COBRA algorithm introduced by \\citet{biau2016cobra}, which combined estimators based on a notion of proximity of predictions on the training data. While the COBRA algorithm used a binary threshold to declare which training data were close and to be used, we generalize this idea by using a kernel to better encapsulate the proximity information. Such a smoothing kernel provides more representative weights to each of the training points which are used to build the aggregate and final predictor, and \\texttt{KernelCobra} systematically outperforms the COBRA algorithm. While COBRA is intended for regression, \\texttt{KernelCobra} deals with classification and regression. \\texttt{KernelCobra} is included as part of the open source Python package \\texttt{Pycobra} (0.2.4 and onward), introduced by \\citet{guedj2018pycobra}. Numerical experiments assess the performance (in terms of pure prediction and computational complexity) of \\texttt{KernelCobra} on real-life and synthetic datasets.","url_abs":"https://arxiv.org/abs/1912.08311v1","url_pdf":"https://arxiv.org/pdf/1912.08311v1.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":"kernel-based-ensemble-learning-in-python","repo_url":"https://github.com/bhargavvader/pycobra","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}