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  "Title": "Identify Influential Observations in Binary Classification",
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  "Description": "Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018)\n<doi:10.1080/10543406.2017.1377728> provide two theoretical\nmethods (influence function and local influence) based on the\narea under the receiver operating characteristic curve (AUC) to\nquantify the numerical impact of each observation to the\noverall AUC. Alternative graphical tools, cumulative lift\ncharts, are proposed to reveal the existences and approximate\nlocations of those influential observations through data\nvisualization.",
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