influenceAUC: Identify Influential Observations in Binary Classification

Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.

Getting started

Package details

AuthorBo-Shiang Ke [cre, aut, cph], Yuan-chin Ivan Chang [aut], Wen-Ting Wang [aut]
MaintainerBo-Shiang Ke <>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the influenceAUC package in your browser

Any scripts or data that you put into this service are public.

influenceAUC documentation built on July 1, 2020, 6 p.m.