{"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/an-infra-structure-for-performance-estimation","title":"An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R","arxiv_id":"1412.0436","date":"2014-12-01","proceeding":null,"authors":["Luis Torgo"],"abstract":"This document describes an infra-structure provided by the R package\nperformanceEstimation that allows to estimate the predictive performance of\ndifferent approaches (workflows) to predictive tasks. The infra-structure is\ngeneric in the sense that it can be used to estimate the values of any\nperformance metrics, for any workflow on different predictive tasks, namely,\nclassification, regression and time series tasks. The package also includes\nseveral standard workflows that allow users to easily set up their experiments\nlimiting the amount of work and information they need to provide. The overall\ngoal of the infra-structure provided by our package is to facilitate the task\nof estimating the predictive performance of different modeling approaches to\npredictive tasks in the R environment.","url_abs":"http://arxiv.org/abs/1412.0436v4","url_pdf":"http://arxiv.org/pdf/1412.0436v4.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":"an-infra-structure-for-performance-estimation","repo_url":"https://github.com/ltorgo/performanceEstimation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"regression-1","task_name":"regression"}],"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}