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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sonia Pérez-Fernández & Pablo Martínez-Camblor & Peter Filzmoser & Norberto Corral, 2021. "Visualizing the decision rules behind the ROC curves: understanding the classification process," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 135-161, March.
    • Handle: RePEc:spr:alstar:v:105:y:2021:i:1:d:10.1007_s10182-020-00385-2
    DOI: 10.1007/s10182-020-00385-2
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    References listed on IDEAS

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