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ROC curves for regression

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ROC curves for regression

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dc.contributor.author Hernández-Orallo, José es_ES
dc.date.accessioned 2014-09-25T17:23:23Z
dc.date.available 2014-09-25T17:23:23Z
dc.date.issued 2013-12
dc.identifier.issn 0031-3203
dc.identifier.uri http://hdl.handle.net/10251/40252
dc.description “NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Volume 46, Issue 12, December 2013, Pages 3395–3411 DOI: 10.1016/j.patcog.2013.06.014 es_ES
dc.description.abstract Receiver Operating Characteristic (ROC) analysis is one of the most popular tools for the visual assessment and understanding of classifier performance. In this paper we present a new representation of regression models in the so-called regression ROC (RROC) space. The basic idea is to represent over-estimation against under-estimation. The curves are just drawn by adjusting a shift, a constant that is added (or subtracted) to the predictions, and plays a similar role as a threshold in classification. From here, we develop the notions of optimal operating condition, convexity, dominance, and explore several evaluation metrics that can be shown graphically, such as the area over the RROC curve (AOC). In particular, we show a novel and significant result: the AOC is equivalent to the error variance. We illustrate the application of RROC curves to resource estimation, namely the estimation of software project effort. es_ES
dc.description.sponsorship I would like to thank Peter Flach and Nicolas Lachiche for some very useful comments and corrections on earlier versions of this paper, especially the suggestion of drawing normalised curves (dividing x-axis and y-axis by n). This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project Prometeo/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the respective national research councils and ministries. en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Pattern Recognition es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject ROC curves es_ES
dc.subject Cost-sensitive regression es_ES
dc.subject Operating condition es_ES
dc.subject Asymmetric loss es_ES
dc.subject Error variance es_ES
dc.subject MSE decomposition es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title ROC curves for regression es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.patcog.2013.06.014
dc.relation.projectID info:eu-repo/grantAgreement/MEC//CSD2007-00022/ES/Agreement Technologies/
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2010-21062-C02-02/ES/SWEETLOGICS-UPV/
dc.relation.projectID info:eu-repo/grantAgreement/Generalitat Valenciana//PROMETEO08%2F2008%2F051/ES/Advances on Agreement Technologies for Computational Entities (atforce)/
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Hernández-Orallo, J. (2013). ROC curves for regression. Pattern Recognition. 46(12):3395-3411. https://doi.org/10.1016/j.patcog.2013.06.014 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.patcog.2013.06.014 es_ES
dc.description.upvformatpinicio 3395 es_ES
dc.description.upvformatpfin 3411 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 46 es_ES
dc.description.issue 12 es_ES
dc.relation.senia 263081
dc.contributor.funder Generalitat Valenciana
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES


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