RGA: a unified measure of predictive accuracy

Paolo Giudici () and Emanuela Raffinetti ()
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Paolo Giudici: University of Pavia
Emanuela Raffinetti: University of Pavia

Advances in Data Analysis and Classification, 2025, vol. 19, issue 1, No 4, 67-93

Abstract: Abstract A key point to assess statistical forecasts is the evaluation of their predictive accuracy. Recently, a new measure, called Rank Graduation Accuracy (RGA), based on the concordance between the ranks of the predicted values and the ranks of the actual values of a series of observations to be forecast, was proposed for the assessment of the quality of the predictions. In this paper, we demonstrate that, in a classification perspective, when the response to be predicted is binary, the RGA coincides both with the AUROC and the Wilcoxon-Mann–Whitney statistic, and can be employed to evaluate the accuracy of probability forecasts. When the response to be predicted is real valued, the RGA can still be applied, differently from the AUROC, and similarly to measures such as the RMSE. Differently from the RMSE, the RGA measure evaluates point predictions in terms of their ranks, rather than in terms of their values, improving robustness.

Keywords: Concordance curve; Receiver Operating Characteristic Curve; Predictive accuracy; Ordinal classification; 68T01; 68T20; 68T37 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11634-023-00574-2

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