A new approach in model selection for ordinal target variables

Elena Ballante, Silvia Figini () and Pierpaolo Uberti ()
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Elena Ballante: University of Pavia
Silvia Figini: University of Pavia
Pierpaolo Uberti: University of Genova

Computational Statistics, 2022, vol. 37, issue 1, No 3, 43-56

Abstract: 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.

Keywords: Classification; Ordinal data; Performance index; Model assessment (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01112-4

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