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A rank graduation accuracy measure

Author

Listed:
  • Arianna Agosto

    (Università di Pavia)

  • Paolo Giudici

    (Università di Pavia)

  • Emanuela Raffinetti

    (University of Milan)

Abstract

A key point in the application of data science models is the evaluation of their accuracy. Statistics and machine learning have provided, over the years, a number of summary measures aimed at measuring the accuracy of a model in terms of its predictions, such as the Area under the ROC curve and the Somers' coefficient. Our aim is to present an alternative measure, based on the distance between the predicted and the observed ranks of the response variable, which can improve model accuracy in challenging real world applications.

Suggested Citation

  • Arianna Agosto & Paolo Giudici & Emanuela Raffinetti, 2020. "A rank graduation accuracy measure," DEM Working Papers Series 179, University of Pavia, Department of Economics and Management.
    • Handle: RePEc:pav:demwpp:demwp0179
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    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0179.pdf
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    More about this item

    Keywords

    Predictive accuracy; Concordance measures; Credit Scoring;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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