influenceAUC

Identify Influential Observations in Binary Classification

CRAN Package

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.

  • Version0.1.2
  • R versionunknown
  • LicenseGPL-3
  • Needs compilation?No
  • Last release05/30/2020

Team


Insights

Last 30 days

Last 365 days

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Data provided by CRAN


Binaries


Dependencies

  • Imports6 packages