{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T11:00:21Z","timestamp":1772881221134,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In Machine Learning, prediction quality is usually measured using different techniques and evaluation methods. In the regression models, the goal is to minimize the distance between the actual and predicted value. This error evaluation technique lacks a detailed evaluation of the type of errors that occur on specific data. This paper will introduce a simple regularization term to manage the number of over-predicted\/under-predicted instances in a regression model.<\/jats:p>","DOI":"10.1186\/s40537-023-00685-9","type":"journal-article","created":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T18:02:54Z","timestamp":1674928974000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Error and optimism bias regularization"],"prefix":"10.1186","volume":"10","author":[{"given":"Nassim","family":"Sohaee","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"685_CR1","doi-asserted-by":"crossref","unstructured":"Ito S, Fujimaki R. Optimization beyond prediction: Prescriptive price optimization. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 1833\u20131841.","DOI":"10.1145\/3097983.3098188"},{"key":"685_CR2","first-page":"15","volume":"380","author":"S Morris","year":"2019","unstructured":"Morris S, Kumari T. Overestimation in the growth rates of national income in recent years?\u2013an analyses based on extending gdp04\u201305 through other indicators of output. Indian Institute of Management Ahmedabad. 2019;380:15.","journal-title":"Indian Institute of Management Ahmedabad"},{"issue":"1","key":"685_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-016-0028-x","volume":"7","author":"Z-R Xie","year":"2017","unstructured":"Xie Z-R, Chen J, Wu Y. Predicting protein\u2013protein association rates using coarse-grained simulation and machine learning. Sci Rep. 2017;7(1):1\u201317.","journal-title":"Sci Rep"},{"issue":"1","key":"685_CR4","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/S0301-2115(99)00247-X","volume":"91","author":"E Sheiner","year":"2000","unstructured":"Sheiner E, Sheiner EK, Hershkovitz R, Mazor M, Katz M, ShohamVardi I. Overestimation and underestimation of labor pain. Eur J Obstetr Gynecol Reprod Biol. 2000;91(1):37\u201340.","journal-title":"Eur J Obstetr Gynecol Reprod Biol"},{"key":"685_CR5","unstructured":"Armstrong TB, Koles\u00b4ar M, Kwon S. Bias-aware inference in regularized regression models. arXiv preprint arXiv:2012.14823. 2020."},{"issue":"1","key":"685_CR6","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. J Roy Stat Soc: Ser B (Methodol). 1996;58(1):267\u201388.","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"issue":"6","key":"685_CR7","first-page":"2313","volume":"35","author":"E Candes","year":"2007","unstructured":"Candes E, Tao T. The dantzig selector: Statistical estimation when p is much larger than n. Ann Stat. 2007;35(6):2313\u201351.","journal-title":"Ann Stat"},{"issue":"3","key":"685_CR8","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1080\/00401706.1973.10489073","volume":"15","author":"GC McDonald","year":"1973","unstructured":"McDonald GC, Schwing RC. Instabilities of regression estimates relating air pollution to mortality. Technometrics. 1973;15(3):463\u201381.","journal-title":"Technometrics"},{"issue":"1","key":"685_CR9","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1111\/j.2517-6161.1974.tb00990.x","volume":"36","author":"CM Theobald","year":"1974","unstructured":"Theobald CM. Generalizations of mean square error applied to ridge regression. J Roy Stat Soc: Ser B (Methodol). 1974;36(1):103\u20136.","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"issue":"5","key":"685_CR10","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1109\/72.788648","volume":"10","author":"V Cherkassky","year":"1999","unstructured":"Cherkassky V, Shao X, Mulier FM, Vapnik VN. Model complexity control for regression using vc generalization bounds. IEEE Trans Neural Networks. 1999;10(5):1075\u201389.","journal-title":"IEEE Trans Neural Networks"},{"key":"685_CR11","volume-title":"The Nature of Statistical Learning Theory","author":"V Vapnik","year":"1999","unstructured":"Vapnik V. The Nature of Statistical Learning Theory. New York: Springer; 1999."},{"issue":"7","key":"685_CR12","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1016\/j.neunet.2009.04.005","volume":"22","author":"V Cherkassky","year":"2009","unstructured":"Cherkassky V, Ma Y. Another look at statistical learning theory and regularization. Neural Netw. 2009;22(7):958\u201369.","journal-title":"Neural Netw"},{"key":"685_CR13","unstructured":"Vapnik VN. Statistical learning theory. Adaptive and learning systems for signal processing communications and control. 1998."},{"key":"685_CR14","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A. Deep Learning. New York: MIT Press; 2016."},{"issue":"4945","key":"685_CR15","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1126\/science.247.4945.978","volume":"247","author":"T Poggio","year":"1990","unstructured":"Poggio T, Girosi F. Regularization algorithms for learning that are equivalent to multilayer networks. Science. 1990;247(4945):978\u201382.","journal-title":"Science"},{"issue":"1","key":"685_CR16","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s10994-015-5540-x","volume":"103","author":"L Oneto","year":"2016","unstructured":"Oneto L, Ridella S, Anguita D. Tikhonov, ivanov and morozov regularization for support vector machine learning. Mach Learn. 2016;103(1):103\u201336.","journal-title":"Mach Learn"},{"key":"685_CR17","unstructured":"Friedman J, Popescu BE. Gradient directed regularization for linear regression and classification. Technical report, Citeseer. 2003."},{"key":"685_CR18","doi-asserted-by":"crossref","unstructured":"Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory; 1992. p. 144\u201352.","DOI":"10.1145\/130385.130401"},{"key":"685_CR19","first-page":"774","volume":"24","author":"V Vapnik","year":"1963","unstructured":"Vapnik V. Pattern recognition using generalized portrait method. Autom Remote Control. 1963;24:774\u201380.","journal-title":"Autom Remote Control"},{"key":"685_CR20","volume-title":"Theory of pattern recognition","author":"V Vapnik","year":"1974","unstructured":"Vapnik V, Chervonenkis A. Theory of pattern recognition. Moscow: Nauka; 1974."},{"key":"685_CR21","unstructured":"Tsai T, Benjamin J, Jha A. American Hospital Capacity And Projected Need for COVID-19 Patient Care. 2020."},{"issue":"8","key":"685_CR22","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380.","journal-title":"Neural Comput"},{"issue":"1","key":"685_CR23","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3141\/1805-03","volume":"1805","author":"P Lingras","year":"2002","unstructured":"Lingras P, Sharma S, Zhong M. Prediction of recreational travel using genetically designed regression and time-delay neural network models. Transp Res Rec. 2002;1805(1):16\u201324.","journal-title":"Transp Res Rec"},{"issue":"4","key":"685_CR24","doi-asserted-by":"publisher","first-page":"25","DOI":"10.9734\/ajrcos\/2020\/v5i430141","volume":"5","author":"S Dutta","year":"2020","unstructured":"Dutta S, Bandyopadhyay SK, Kim T-H. Cnn-lstm model for verifying predictions of covid-19 cases. Asian J Res Comput Sci. 2020;5(4):25\u201332.","journal-title":"Asian J Res Comput Sci"},{"key":"685_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110212","volume":"140","author":"F Shahid","year":"2020","unstructured":"Shahid F, Zameer A, Muneeb M. Predictions for covid-19 with deep learning models of lstm, gru and bi-lstm. Chaos, Solitons Fractals. 2020;140: 110212.","journal-title":"Chaos, Solitons Fractals"},{"issue":"9","key":"685_CR26","doi-asserted-by":"publisher","first-page":"6438","DOI":"10.1109\/TII.2020.2999442","volume":"17","author":"Y Qin","year":"2020","unstructured":"Qin Y, Chen D, Xiang S, Zhu C. Gated dual attention unit neural networks for remaining useful life prediction of rolling bearings. IEEE Trans Industr Inf. 2020;17(9):6438\u201347.","journal-title":"IEEE Trans Industr Inf"},{"key":"685_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106602","volume":"139","author":"J Zhu","year":"2020","unstructured":"Zhu J, Chen N, Shen C. A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions. Mech Syst Signal Process. 2020;139: 106602.","journal-title":"Mech Syst Signal Process"},{"key":"685_CR28","unstructured":"Islam MM, Prosvirin AE, Kim J-M. Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines. Mechanical Systems and Signal."},{"key":"685_CR29","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., Eklund, N.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 IEEE International Conference on Prognostics and Health Management, 2008. p. 1\u20139.","DOI":"10.1109\/PHM.2008.4711414"},{"key":"685_CR30","doi-asserted-by":"publisher","unstructured":"Sahoo B. Data-Driven Remaining Useful Life (RUL) Prediction. 2020. https:\/\/doi.org\/10.5281\/zenodo.5890595. https:\/\/biswajitsahoo1111.github.io\/rulcodesopen\/a.","DOI":"10.5281\/zenodo.5890595"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00685-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-023-00685-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00685-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T05:57:21Z","timestamp":1728799041000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-023-00685-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,28]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["685"],"URL":"https:\/\/doi.org\/10.1186\/s40537-023-00685-9","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,28]]},"assertion":[{"value":"2 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The author declares that she has no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"8"}}