A rank graduation accuracy measure

Arianna Agosto, Paolo Giudici and Emanuela Raffinetti ()
Additional contact information
Emanuela Raffinetti: University of Milan

No 179, DEM Working Papers Series from University of Pavia, Department of Economics and Management

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 (search for similar items in EconPapers)
JEL-codes: C01 C18 C31 C52 G32 (search for similar items in EconPapers)
Pages: 29
Date: 2020-01
New Economics Papers: this item is included in nep-ore
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0179.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pav:demwpp:demwp0179

Access Statistics for this paper

More papers in DEM Working Papers Series from University of Pavia, Department of Economics and Management Contact information at EDIRC.
Bibliographic data for series maintained by Alice Albonico ( this e-mail address is bad, please contact ).