Package: npcs 0.1.1

npcs: Neyman-Pearson Classification via Cost-Sensitive Learning

We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021).

Authors:Ye Tian [aut], Ching-Tsung Tsai [aut, cre], Yang Feng [aut]

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npcs.pdf |npcs.html✨
npcs/json (API)

# Install 'npcs' in R:
install.packages('npcs', repos = c('https://tctsung.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.23 score 17 scripts 150 downloads 5 exports 82 dependencies

Last updated 2 years agofrom:788e78e789. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-winWARNINGNov 04 2024
R-4.5-linuxWARNINGNov 04 2024
R-4.4-winWARNINGNov 04 2024
R-4.4-macWARNINGNov 04 2024
R-4.3-winWARNINGNov 04 2024
R-4.3-macWARNINGNov 04 2024

Exports:cv.npcserror_rategamma_smotegenerate_datanpcs

Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tabledbscandfoptimdiagramdigestdplyre1071fansifarverFNNforcatsforeachformatRfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatigraphipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartscalesshapesmotefamilySQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

npcs-demo

Rendered fromnpcs-demo.Rmdusingknitr::rmarkdownon Nov 04 2024.

Last update: 2023-04-27
Started: 2021-10-19