Package: evabic 0.1.1

Antoine Bichat

evabic: Evaluation of Binary Classifiers

Evaluates the performance of binary classifiers. Computes confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR, accuracy, F1, DOR, ..), and area under the curve. Outputs are well suited for nested dataframes.

Authors:Antoine Bichat [aut, cre]

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evabic.pdf |evabic.html✨
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NEWS

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

Bug tracker:https://github.com/abichat/evabic/issues

Pkgdown site:https://abichat.github.io

On CRAN:

classifiermeasurespredictorsroc-curvestatistics

3.62 score 6 stars 14 scripts 193 downloads 25 exports 0 dependencies

Last updated 3 years agofrom:128b2ee5a5. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 06 2025
R-4.5-winOKFeb 06 2025
R-4.5-macOKFeb 06 2025
R-4.5-linuxOKFeb 06 2025
R-4.4-winOKFeb 06 2025
R-4.4-macOKFeb 06 2025
R-4.3-winOKFeb 06 2025
R-4.3-macOKFeb 06 2025

Exports:add_namesebc_ACCebc_allmeasuresebc_AUCebc_AUC_from_measuresebc_BACCebc_confusionebc_DORebc_F1ebc_FDRebc_FNebc_FNRebc_FORebc_FPebc_FPRebc_NLRebc_NPVebc_PLRebc_PPVebc_tidyebc_tidy_by_thresholdebc_TNebc_TNRebc_TPebc_TPR

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Add names to a vectoradd_names
Available measuresebc_allmeasures
Area under the curveebc_AUC ebc_AUC_from_measures
Confusion matrixebc_confusion
Tidy output for measuresebc_tidy
Measures by thresholdebc_tidy_by_threshold
Confusion measures.ebc_FN ebc_FP ebc_TN ebc_TP
Derived measures.ebc_ACC ebc_BACC ebc_DOR ebc_F1 ebc_FDR ebc_FNR ebc_FOR ebc_FPR ebc_NLR ebc_NPV ebc_PLR ebc_PPV ebc_TNR ebc_TPR