ROC APP: AN APPLICATION TO UNDERSTAND ROC CURVES
Main Article Content
Abstract
We present a software application (https://gfvonborries.shinyapps.io/roc_app/) to help students understand the Receiver Operating Characteristic (ROC) curve and other concepts associated with binary classification models. We use the diagnostic test scenario as a motivation to explain the underlying concepts and the app functionalities. The ROC App enables students to interactively learn why/how the ROC curve closely relates to the accuracy rates, by seeing how these curves and rates respond to modifications on the population’s parameters.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
ARNHOLT, A. Using a shiny app to teach the concept of power.Teaching Statistics,v.41, 2018.
CHANG, W.; CHENG, J.; ALLAIRE, J. J.; XIE, Y.; MCPHERSON, J.Shiny: Web application framework for r. R package version 1.5.0. 2020.
FAWCETT, L. Using interactive shiny applications to facilitate research-informed learning and teaching. Journal of Statistics Education. v.26, n.1, p.2-16, 2018.
FREIRE, S. Using shiny to illustrate the probability density function concept: Probability density function.Teaching Statistics, v.41, 2018.
NETTLEMAN, M. D. Receiver operator characteristic (roc) curves. Infection Control and Hospital Epidemiology, v.9, n.8, p.374-377, 1988.
R CORE TEAM. A R: language and environment for statistical computing. Vienna, Austria. 2020.https://www.R-project.org/.
WATSON, J. W.; WHITING, P. F.; BRUSH, J. E. Interpreting a covid-19 test result. British Medical Journal, v.369, n.1808, 2020.