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]

npcs_0.1.1.tar.gz
npcs_0.1.1.zip(r-4.7-any)npcs_0.1.1.zip(r-4.6-any)npcs_0.1.1.zip(r-4.5-any)
npcs_0.1.1.tgz(r-4.6-any)npcs_0.1.1.tgz(r-4.5-any)
npcs_0.1.1.tar.gz(r-4.7-any)npcs_0.1.1.tar.gz(r-4.6-any)
npcs_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
npcs/json (API)

# Install 'npcs' in R:

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

On CRAN:

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

2.18 score 15 scripts 291 downloads 5 exports 80 dependencies

Last updated from:788e78e789. Checks:7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	WARNING	183
source / vignettes	OK	268
linux-release	WARNING	193
macos-release	WARNING	218
macos-oldrel	WARNING	167
windows-devel	WARNING	118
windows-release	WARNING	90
windows-oldrel	WARNING	115
wasm-release	OK	134

Exports:cv.npcs error_rate gamma_smote generate_data npcs

Dependencies:caret class cli clock codetools cpp11 data.table dbscan dfoptim diagram digest dplyr e1071 farver FNN forcats foreach formatR future future.apply generics ggplot2 globals glue gower gtable hardhat igraph ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix ModelMetrics nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp recipes reshape2 rlang rpart S7 scales shape smotefamily sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

npcs-demo

Installation | Introduction | A Simple Example | Reference

Rendered fromnpcs-demo.Rmdusingknitr::rmarkdown

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

Help page	Topics
Compare the performance of the NPMC-CX, NPMC-ER, and vanilla models through cross-validation or bootstrapping methods	cv.npcs
Calculate the error rates for each class.	error_rate
Gamma-synthetic minority over-sampling technique (gamma-SMOTE).	gamma_smote
Generate the data.	generate_data
Fit a multi-class Neyman-Pearson classifier with error controls via cost-sensitive learning.	npcs
Predict new labels from new data based on the fitted NPMC classifier.	predict.npcs
Print the cv.npcs object.	print.cv.npcs

Package: npcs 0.1.1

npcs: Neyman-Pearson Classification via Cost-Sensitive Learning

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Help Manual

Usage by other packages (reverse dependencies)