Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation
Data that have not been modeled cannot be correctly predicted. Under this assumption, this research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions in the training and test sets to be different and, therefore, a deteriorati...
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| Published in: | Mathematics (Basel) Vol. 10; no. 14; p. 2538 |
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| Abstract | Data that have not been modeled cannot be correctly predicted. Under this assumption, this research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions in the training and test sets to be different and, therefore, a deterioration of the model performance estimation. Even though the stratification of the output variable is widely used in the field of classification to reduce the impacts of dataset shift induced by cross-validation, its use in regression is not widespread in the literature. This paper analyzes the consequences for dataset shift of including different regressand stratification schemes in cross-validation with regression data. The results obtained show that these allow for creating more similar training and test sets, reducing the presence of dataset shift related to cross-validation. The bias and deviation of the performance estimation results obtained by regression algorithms are improved using the highest amounts of strata, as are the number of cross-validation repetitions necessary to obtain these better results. |
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| AbstractList | Data that have not been modeled cannot be correctly predicted. Under this assumption, this research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions in the training and test sets to be different and, therefore, a deterioration of the model performance estimation. Even though the stratification of the output variable is widely used in the field of classification to reduce the impacts of dataset shift induced by cross-validation, its use in regression is not widespread in the literature. This paper analyzes the consequences for dataset shift of including different regressand stratification schemes in cross-validation with regression data. The results obtained show that these allow for creating more similar training and test sets, reducing the presence of dataset shift related to cross-validation. The bias and deviation of the performance estimation results obtained by regression algorithms are improved using the highest amounts of strata, as are the number of cross-validation repetitions necessary to obtain these better results. |
| Author | Romero-Béjar, José L. Sáez, José A. |
| Author_xml | – sequence: 1 givenname: José A. orcidid: 0000-0002-4592-1538 surname: Sáez fullname: Sáez, José A. – sequence: 2 givenname: José L. orcidid: 0000-0002-5310-9638 surname: Romero-Béjar fullname: Romero-Béjar, José L. |
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| References | Arlot (ref_5) 2010; 4 ref_14 Derrac (ref_47) 2011; 1 Herrera (ref_8) 2012; 23 ref_13 Wu (ref_33) 2013; 49 ref_17 ref_16 ref_15 Snee (ref_29) 1977; 19 Liu (ref_1) 2020; 42 Baxter (ref_22) 2002; 1 Shimodaira (ref_38) 2000; 90 Krstajic (ref_7) 2014; 6 Baringhaus (ref_25) 2018; 81 Maldonado (ref_9) 2022; 52 Xu (ref_26) 2018; 183 Joseph (ref_31) 2022; 64 ref_23 Jiang (ref_4) 2017; 69 Carrizosa (ref_19) 2022; 203 May (ref_27) 2010; 23 Kang (ref_18) 2018; 432 Sahoo (ref_30) 2012; 39 Sugiyama (ref_37) 2007; 20 Kanamori (ref_39) 2009; 10 Rad (ref_2) 2020; 82 ref_36 Smola (ref_48) 2004; 14 Diamantidis (ref_28) 2000; 116 ref_32 Chapaneri (ref_35) 2019; 7 Qi (ref_3) 2019; 7 Huang (ref_40) 2006; 19 Breiman (ref_21) 1992; 60 Zheng (ref_34) 2018; 54 Wei (ref_10) 2021; 42 Dimitrova (ref_46) 2020; 95 Andries (ref_12) 2021; 35 ref_45 ref_44 Raeder (ref_11) 2012; 45 ref_43 ref_41 Zeng (ref_42) 2000; 12 ref_49 Ding (ref_24) 2015; 44 Dhanjal (ref_20) 2016; 19 ref_6 |
| References_xml | – ident: ref_43 doi: 10.3390/math9050547 – volume: 64 start-page: 166 year: 2022 ident: ref_31 article-title: SPlit: An optimal method for data splitting publication-title: Technometrics doi: 10.1080/00401706.2021.1921037 contributor: fullname: Joseph – ident: ref_32 – volume: 19 start-page: 601 year: 2006 ident: ref_40 article-title: Correcting sample selection bias by unlabeled data publication-title: Adv. Neural Inf. Process. Syst. contributor: fullname: Huang – volume: 4 start-page: 40 year: 2010 ident: ref_5 article-title: A survey of cross-validation procedures for model selection publication-title: Stat. Surv. doi: 10.1214/09-SS054 contributor: fullname: Arlot – volume: 203 start-page: 117423 year: 2022 ident: ref_19 article-title: The tree based linear regression model for hierarchical categorical variables publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.117423 contributor: fullname: Carrizosa – volume: 69 start-page: 94 year: 2017 ident: ref_4 article-title: Error estimation based on variance analysis of k-fold cross-validation publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.03.025 contributor: fullname: Jiang – ident: ref_16 – volume: 14 start-page: 199 year: 2004 ident: ref_48 article-title: A tutorial on support vector regression publication-title: Stat. Comput. doi: 10.1023/B:STCO.0000035301.49549.88 contributor: fullname: Smola – volume: 7 start-page: 33454 year: 2019 ident: ref_3 article-title: On estimating model in feature selection with cross-validation publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2892062 contributor: fullname: Qi – volume: 116 start-page: 1 year: 2000 ident: ref_28 article-title: Unsupervised stratification of cross-validation for accuracy estimation publication-title: Artif. Intell. doi: 10.1016/S0004-3702(99)00094-6 contributor: fullname: Diamantidis – volume: 54 start-page: 1013 year: 2018 ident: ref_34 article-title: On lack of robustness in hydrological model development due to absence of guidelines for selecting calibration and evaluation data: Demonstration for data-driven models publication-title: Water Resour. Res. doi: 10.1002/2017WR021470 contributor: fullname: Zheng – volume: 45 start-page: 521 year: 2012 ident: ref_11 article-title: A unifying view on dataset shift in classification publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2011.06.019 contributor: fullname: Raeder – volume: 90 start-page: 227 year: 2000 ident: ref_38 article-title: Improving predictive inference under covariate shift by weighting the log-likelihood function publication-title: J. Stat. Plan. Inference doi: 10.1016/S0378-3758(00)00115-4 contributor: fullname: Shimodaira – volume: 49 start-page: 7598 year: 2013 ident: ref_33 article-title: A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks publication-title: Water Resour. Res. doi: 10.1002/2012WR012713 contributor: fullname: Wu – volume: 7 start-page: 73804 year: 2019 ident: ref_35 article-title: Covariate shift adaptation for structured regression with Frank-Wolfe algorithms publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2920486 contributor: fullname: Chapaneri – volume: 42 start-page: 1083 year: 2020 ident: ref_1 article-title: Fast cross-validation for kernel-based algorithms publication-title: IEEE Trans. Pattern Anal. Mach. Intell. contributor: fullname: Liu – volume: 1 start-page: 201 year: 2002 ident: ref_22 article-title: Developing artificial neural network models of water treatment processes: A guide for utilities publication-title: J. Environ. Eng. Sci. doi: 10.1139/s02-014 contributor: fullname: Baxter – volume: 60 start-page: 291 year: 1992 ident: ref_21 article-title: Submodel selection and evaluation in regression. The x-random case publication-title: Int. Stat. Rev. doi: 10.2307/1403680 contributor: fullname: Breiman – volume: 12 start-page: 1 year: 2000 ident: ref_42 article-title: Distribution-balanced stratified cross-validation for accuracy estimation publication-title: J. Exp. Theor. Artif. Intell. doi: 10.1080/095281300146272 contributor: fullname: Zeng – volume: 44 start-page: 103 year: 2015 ident: ref_24 article-title: Extreme learning machine: Algorithm, theory and applications publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-013-9405-z contributor: fullname: Ding – ident: ref_41 – volume: 1 start-page: 3 year: 2011 ident: ref_47 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2011.02.002 contributor: fullname: Derrac – ident: ref_13 – volume: 81 start-page: 891 year: 2018 ident: ref_25 article-title: Efficiency comparison of the Wilcoxon tests in paired and independent survey samples publication-title: Metrika doi: 10.1007/s00184-018-0661-4 contributor: fullname: Baringhaus – ident: ref_44 doi: 10.3390/math9212696 – volume: 42 start-page: 3326 year: 2021 ident: ref_10 article-title: L2-norm prototypical networks for tackling the data shift problem in scene classification publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2020.1871097 contributor: fullname: Wei – volume: 183 start-page: 29 year: 2018 ident: ref_26 article-title: Representative splitting cross validation publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2018.10.008 contributor: fullname: Xu – ident: ref_17 doi: 10.1007/978-0-387-21606-5 – volume: 23 start-page: 1304 year: 2012 ident: ref_8 article-title: Study on the impact of partition-induced dataset shift on k-fold cross-validation publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2012.2199516 contributor: fullname: Herrera – ident: ref_23 doi: 10.1201/9781315139470 – volume: 10 start-page: 1391 year: 2009 ident: ref_39 article-title: A least-squares approach to direct importance estimation publication-title: J. Mach. Learn. Res. contributor: fullname: Kanamori – ident: ref_14 – volume: 432 start-page: 199 year: 2018 ident: ref_18 article-title: Locally linear ensemble for regression publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.12.022 contributor: fullname: Kang – ident: ref_45 doi: 10.1201/9780367816377 – ident: ref_6 – ident: ref_49 doi: 10.1145/2939672.2939785 – volume: 39 start-page: 7004 year: 2012 ident: ref_30 article-title: A data clustering algorithm for stratified data partitioning in artificial neural network publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2012.01.047 contributor: fullname: Sahoo – volume: 19 start-page: 41 year: 2016 ident: ref_20 article-title: An empirical comparison of V-fold penalisation and cross-validation for model selection in distribution-free regression publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-014-0381-y contributor: fullname: Dhanjal – volume: 35 start-page: e3373 year: 2021 ident: ref_12 article-title: A chemometrician’s guide to transfer learning publication-title: J. Chemom. doi: 10.1002/cem.3373 contributor: fullname: Andries – volume: 20 start-page: 1 year: 2007 ident: ref_37 article-title: Direct importance estimation with model selection and its application to covariate shift adaptation publication-title: Adv. Neural Inf. Process. Syst. contributor: fullname: Sugiyama – volume: 82 start-page: 965 year: 2020 ident: ref_2 article-title: A scalable estimate of the out-of-sample prediction error via approximate leave-one-out cross-validation publication-title: J. R. Stat. Soc. Ser. B Stat. Methodol. doi: 10.1111/rssb.12374 contributor: fullname: Rad – volume: 23 start-page: 283 year: 2010 ident: ref_27 article-title: Data splitting for artificial neural networks using SOM-based stratified sampling publication-title: Neural Netw. doi: 10.1016/j.neunet.2009.11.009 contributor: fullname: May – ident: ref_15 – volume: 52 start-page: 5770 year: 2022 ident: ref_9 article-title: Out-of-time cross-validation strategies for classification in the presence of dataset shift publication-title: Appl. Intell. doi: 10.1007/s10489-021-02735-2 contributor: fullname: Maldonado – ident: ref_36 – volume: 19 start-page: 415 year: 1977 ident: ref_29 article-title: Validation of regression models: Methods and examples publication-title: Technometrics doi: 10.1080/00401706.1977.10489581 contributor: fullname: Snee – volume: 6 start-page: 10 year: 2014 ident: ref_7 article-title: Cross-validation pitfalls when selecting and assessing regression and classification models publication-title: J. Cheminform. doi: 10.1186/1758-2946-6-10 contributor: fullname: Krstajic – volume: 95 start-page: 1 year: 2020 ident: ref_46 article-title: Computing the Kolmogorov-Smirnov distribution when the underlying CDF is purely discrete, mixed, or continuous publication-title: J. Stat. Softw. doi: 10.18637/jss.v095.i10 contributor: fullname: Dimitrova |
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| SubjectTerms | Algorithms cross-validation dataset shift Datasets Decision trees Mathematics Neural networks Regression Stratification target shift Test sets Training |
| Title | Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation |
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