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.
- Version0.1.2
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release05/30/2020
Team
Bo-Shiang Ke
Wen-Ting Wang
Show author detailsRolesAuthorYuan-chin Ivan Chang
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- Imports5 packages