{"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/chemmodlab-a-cheminformatics-modeling","title":"chemmodlab: A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models","arxiv_id":"1807.00243","date":"2018-06-30","proceeding":null,"authors":["Jeremy R. Ash Jacqueline M. Hughes-Oliver"],"abstract":"The goal of chemmodlab is to streamline the fitting and assessment pipeline\nfor many machine learning models in R, making it easy for researchers to\ncompare the utility of new models. While focused on implementing methods for\nmodel fitting and assessment that have been accepted by experts in the\ncheminformatics field, all of the methods in chemmodlab have broad utility for\nthe machine learning community. chemmodlab contains several assessment\nutilities including a plotting function that constructs accumulation curves and\na function that computes many performance measures. The most novel feature of\nchemmodlab is the ease with which statistically significant performance\ndifferences for many machine learning models is presented by means of the\nmultiple comparisons similarity plot. Differences are assessed using repeated\nk-fold cross validation where blocking increases precision and multiplicity\nadjustments are applied.","url_abs":"http://arxiv.org/abs/1807.00243v3","url_pdf":"http://arxiv.org/pdf/1807.00243v3.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":"chemmodlab-a-cheminformatics-modeling","repo_url":"https://github.com/jrash/chemmodlab","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"blocking","task_name":"Blocking"}],"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}