A Rank Graduation Accuracy Measure

Posted: 10 Jan 2020

See all articles by Arianna Agosto

Arianna Agosto

University of Pavia

Paolo Giudici

University of Pavia

Emanuela Raffinetti

University of Pavia

Date Written: November 10, 2019

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.

Keywords: Predictive accuracy, Concordance measures, Credit Scoring

Suggested Citation

Agosto, Arianna and Giudici, Paolo and Raffinetti, Emanuela, A Rank Graduation Accuracy Measure (November 10, 2019). Available at SSRN: https://ssrn.com/abstract=3507530 or http://dx.doi.org/10.2139/ssrn.3507530

Arianna Agosto

University of Pavia ( email )

Corso Strada Nuova, 65
27100 Pavia, 27100
Italy

Paolo Giudici

University of Pavia ( email )

Via San Felice 7
27100 Pavia, 27100
Italy

Emanuela Raffinetti (Contact Author)

University of Pavia ( email )

Via San Felice 5
Pavia, 27100
Italy

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