influenceAUC
0.1.2Identify Influential Observations in Binary Classification
Overview
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.
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- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-03-1011 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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14 4 exported
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2.9 avg / 9 max
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14 nodes / 9 edges
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People & History
3 releases. Pick two to compare their code metrics; R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.1.2Latest2020-05-30 · current release
- RR 4.0.0 released · 2020-04-24
- 0.1.12020-02-19 · diff ↗
- 0.1.02020-01-31
Package metadata
- First published
- 2020-01-31
- Total releases
- 3 / 6 yrs
- License
- GPL-3 OSI
- Download size
- not tracked yet
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