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The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

PeerJ. Computer science, 2021-07, Vol.7, p.e623-e623, Article e623 [Peer Reviewed Journal]

COPYRIGHT 2021 PeerJ. Ltd. ;2021 Chicco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 Chicco et al. 2021 Chicco et al. ;ISSN: 2376-5992 ;EISSN: 2376-5992 ;DOI: 10.7717/peerj-cs.623 ;PMID: 34307865

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  • Title:
    The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
  • Author: Chicco, Davide ; Warrens, Matthijs J ; Jurman, Giuseppe
  • Subjects: Analysis ; Artificial Intelligence ; Business metrics ; Classification ; Coefficient of determination ; Coefficients ; Data Mining and Machine Learning ; Data Science ; Electronic health records ; Error analysis ; Machine learning ; Mathematical analysis ; Mean absolute error ; Mean square error ; Regression ; Regression analysis ; Regression evaluation ; Regression evaluation rates ; Root-mean-square errors ; Variables
  • Is Part Of: PeerJ. Computer science, 2021-07, Vol.7, p.e623-e623, Article e623
  • Description: Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R -squared or R 2 ) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R 2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination ( R -squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R -squared as standard metric to evaluate regression analyses in any scientific domain.
  • Publisher: San Diego: PeerJ. Ltd
  • Language: English
  • Identifier: ISSN: 2376-5992
    EISSN: 2376-5992
    DOI: 10.7717/peerj-cs.623
    PMID: 34307865
  • Source: AUTh Library subscriptions: ProQuest Central
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