Identify Influential Observations in Binary Classification

Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) 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.


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install.packages("influenceAUC")

0.1.2 by Bo-Shiang Ke, 5 months ago


Report a bug at https://github.com/BoShiangKe/InfluenceAUC/issues


Browse source code at https://github.com/cran/influenceAUC


Authors: Bo-Shiang Ke [cre, aut, cph] , Yuan-chin Ivan Chang [aut] , Wen-Ting Wang [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports dplyr, geigen, ggplot2, ggrepel, methods, ROCR


See at CRAN