Visualizing the decision rules behind the ROC curves: understanding the classification process

Sonia Pérez-Fernández (), Pablo Martínez-Camblor, Peter Filzmoser and Norberto Corral
Additional contact information
Sonia Pérez-Fernández: University of Oviedo
Pablo Martínez-Camblor: Geisel School of Medicine at Dartmouth
Peter Filzmoser: Vienna University of Technology
Norberto Corral: University of Oviedo

AStA Advances in Statistical Analysis, 2021, vol. 105, issue 1, No 6, 135-161

Abstract: Abstract The receiver operating characteristic (ROC) curve is a graphical method commonly used to study the capacity of continuous variables (markers) to properly classify subjects into one of two groups. The decision made is ultimately endorsed by a classification subset on the space where the marker is defined. In this paper, we study graphical representations and propose visual forms to reflect those classification rules giving rise to the construction of the ROC curve. On the one hand, we use static pictures for displaying the classification regions for univariate markers, which are specially convenient when there is not a monotone relationship between the marker and the likelihood of belonging to one group. In those cases, there are two options to improve the classification accuracy: to allow for more flexibility in the classification rules (for example considering two cutoff points instead of one) or to transform the marker by using a function whose resulting ROC curve is optimal. On the other hand, we propose to build videos for visualizing the collection of subsets when several markers are considered simultaneously. A compilation of techniques for finding a rule that maximizes the area under the ROC curve is included, with a focus on linear combinations. We present a tool for the R software which generates those graphics, and we apply it to one real dataset. The R code is provided as Supplementary Material.

Keywords: Area under the curve; Classification regions; Graphical animations; Multivariate marker; Receiver operating characteristic curve (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10182-020-00385-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:alstar:v:105:y:2021:i:1:d:10.1007_s10182-020-00385-2

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10182/PS2

DOI: 10.1007/s10182-020-00385-2

Access Statistics for this article

AStA Advances in Statistical Analysis is currently edited by Göran Kauermann and Yarema Okhrin

More articles in AStA Advances in Statistical Analysis from Springer, German Statistical Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().