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.


Reference manual

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0.1.2 by Bo-Shiang Ke, a year ago

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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