BoShiangKe/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
Browse package contents
Package details |
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| Maintainer | Bo-Shiang Ke <naivete0907@gmail.com> |
| License | GPL-3 |
| Version | 0.1.3 |
| URL | https://boshiangke.github.io/InfluenceAUC/ |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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