{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T22:18:24Z","timestamp":1785881904931,"version":"3.56.0"},"reference-count":14,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this technology to students and practitioners. We here propose a hands-on training for overfitting that is suitable for introductory level courses and can be carried out on its own or embedded within any data science course. We use workflow-based design of machine learning pipelines, experimentation-based teaching, and hands-on approach that focuses on concepts rather than underlying mathematics. We here detail the data analysis workflows we use in training and motivate them from the viewpoint of teaching goals. Our proposed approach relies on Orange, an open-source data science toolbox that combines data visualization and machine learning, and that is tailored for education in machine learning and explorative data analysis.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008671","type":"journal-article","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T18:26:20Z","timestamp":1614882380000},"page":"e1008671","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":67,"title":["Hands-on training about overfitting"],"prefix":"10.1371","volume":"17","author":[{"given":"Janez","family":"Dem\u0161ar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5864-7056","authenticated-orcid":true,"given":"Bla\u017e","family":"Zupan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"issue":"1","key":"pcbi.1008671.ref001","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1093\/bib\/bbk007","article-title":"Machine learning in bioinformatics","volume":"7","author":"P Larra\u00f1aga","year":"2006","journal-title":"Brief Bioinform"},{"key":"pcbi.1008671.ref002","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.inffus.2018.09.012","article-title":"Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities","volume":"50","author":"M Zitnik","year":"2019","journal-title":"Information Fusion"},{"issue":"10","key":"pcbi.1008671.ref003","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2347736.2347755","article-title":"A few useful things to know about machine learning","volume":"55","author":"P Domingos","year":"2012","journal-title":"Commun ACM."},{"issue":"1","key":"pcbi.1008671.ref004","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1093\/jnci\/95.1.14","article-title":"Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification","volume":"95","author":"R Simon","year":"2003","journal-title":"J Natl Cancer Inst"},{"key":"pcbi.1008671.ref005","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13040-017-0155-3","article-title":"Ten quick tips for machine learning in computational biology","volume":"10","author":"D Chicco","year":"2017","journal-title":"BioData Mining"},{"issue":"3","key":"pcbi.1008671.ref006","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1093\/bioinformatics\/bth474","article-title":"Microarray data mining with visual programming","volume":"21","author":"T Curk","year":"2005","journal-title":"Bioinformatics"},{"key":"pcbi.1008671.ref007","first-page":"35","article-title":"scOrange\u2014A tool for hands-on training of concepts from single-cell data analytics","author":"M Strazar","year":"2019","journal-title":"Bioinformatics"},{"issue":"10","key":"pcbi.1008671.ref008","doi-asserted-by":"crossref","first-page":"4551","DOI":"10.1038\/s41467-019-12397-x","article-title":"Democratized image analytics by visual programming through integration of deep models and small-scale machine learning","author":"P Godec","year":"2019","journal-title":"Nat Comm."},{"key":"pcbi.1008671.ref009","first-page":"2349","article-title":"Orange: Data Mining Toolbox in Python","volume":"14","author":"J Dem\u0161ar","year":"2013","journal-title":"J Mach Learn Res"},{"issue":"1","key":"pcbi.1008671.ref010","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1073\/pnas.97.1.262","article-title":"Knowledge-based analysis of microarray gene expression data by using support vector machines","volume":"97","author":"MP Brown","year":"2000","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"3","key":"pcbi.1008671.ref011","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1093\/bioinformatics\/bti016","article-title":"VizRank: Finding informative data projections in functional genomics by machine learning","volume":"21","author":"G Leban","year":"2005","journal-title":"Bioinformatics"},{"issue":"6","key":"pcbi.1008671.ref012","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1200\/JCO.2005.03.156","article-title":"Patterns of resistance and incomplete response to docetaxel by gene expression profiling in breast cancer patients","volume":"23","author":"JC Chang","year":"2005","journal-title":"J Clin Oncol"},{"key":"pcbi.1008671.ref013","first-page":"319","volume-title":"Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007)","author":"MR Berthold","year":"2007"},{"key":"pcbi.1008671.ref014","doi-asserted-by":"crossref","unstructured":"Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T. YALE: rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. 2006;935\u201340.","DOI":"10.1145\/1150402.1150531"}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1008671","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T18:26:43Z","timestamp":1614882403000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1008671"}},"subtitle":[],"editor":[{"given":"Patricia M.","family":"Palagi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,3,4]]},"references-count":14,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3,4]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1008671","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,4]]}}}