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Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation

Author

Listed:
  • José A. Sáez

    (Department of Statistics and Operations Research, University of Granada, Fuentenueva s/n, 18071 Granada, Spain)

  • José L. Romero-Béjar

    (Department of Statistics and Operations Research, University of Granada, Fuentenueva s/n, 18071 Granada, Spain
    ibs.GRANADA —Instituto de Investigación Biosanitaria, 18012 Granada, Spain
    IMAG—Institute of Mathematics of the University of Granada, Ventanilla 11, 18001 Granada, Spain)

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.

Suggested Citation

  • José A. Sáez & José L. Romero-Béjar, 2022. "Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation," Mathematics, MDPI, vol. 10(14), pages 1-14, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2538-:d:868165
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    References listed on IDEAS

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    1. Silvia Curteanu & Florin Leon & Andra-Maria Mircea-Vicoveanu & Doina Logofătu, 2021. "Regression Methods Based on Nearest Neighbors with Adaptive Distance Metrics Applied to a Polymerization Process," Mathematics, MDPI, vol. 9(5), pages 1-20, March.
    2. Nawin Raj & Zahra Gharineiat, 2021. "Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines," Mathematics, MDPI, vol. 9(21), pages 1-18, October.
    3. Ludwig Baringhaus & Daniel Gaigall, 2018. "Efficiency comparison of the Wilcoxon tests in paired and independent survey samples," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(8), pages 891-930, November.
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