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A new approach in model selection for ordinal target variables

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  • Elena Ballante

    (University of Pavia)

  • Silvia Figini

    (University of Pavia)

  • Pierpaolo Uberti

    (University of Genova)

Abstract

Multi-class predictive models are generally evaluated averaging binary classification indicators without a distinction between nominal and ordinal dependent variables. This paper introduces a novel approach to assess performances of predictive models characterized by an ordinal target variable and a new index for model evaluation is proposed. The new index satisfies mathematical properties and it can be applied to the evaluation of parametric and non parametric models. In order to show how our performance indicator works, empirical evidences obtained on toy examples and simulated data are provided. On the basis of the results achieved, we underline that our approach can be a more suitable criterion for model selection than the performance indexes currently suggested in the literature.

Suggested Citation

  • Elena Ballante & Silvia Figini & Pierpaolo Uberti, 2022. "A new approach in model selection for ordinal target variables," Computational Statistics, Springer, vol. 37(1), pages 43-56, March.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:1:d:10.1007_s00180-021-01112-4
    DOI: 10.1007/s00180-021-01112-4
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    References listed on IDEAS

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    1. Adolfo Morrone & Alfonso Piscitelli & Antonio D’Ambrosio, 2019. "How Disadvantages Shape Life Satisfaction: An Alternative Methodological Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 477-502, January.
    2. Gigliarano, Chiara & Figini, Silvia & Muliere, Pietro, 2014. "Making classifier performance comparisons when ROC curves intersect," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 300-312.
    3. D. J. Hand, 2001. "Measuring Diagnostic Accuracy of Statistical Prediction Rules," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(1), pages 3-16, March.
    4. Galimberti, Giuliano & Soffritti, Gabriele & Maso, Matteo Di, 2012. "Classification Trees for Ordinal Responses in R: The rpartScore Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i10).
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    Cited by:

    1. Rosaria Simone, 2023. "Uncertainty Diagnostics of Binomial Regression Trees for Ordered Rating Data," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 79-105, April.

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