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RGA: a unified measure of predictive accuracy

Author

Listed:
  • Paolo Giudici

    (University of Pavia)

  • Emanuela Raffinetti

    (University of Pavia)

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.

Suggested Citation

  • Paolo Giudici & Emanuela Raffinetti, 2025. "RGA: a unified measure of predictive accuracy," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 19(1), pages 67-93, March.
    • Handle: RePEc:spr:advdac:v:19:y:2025:i:1:d:10.1007_s11634-023-00574-2
    DOI: 10.1007/s11634-023-00574-2
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