Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.
Version: 2.1.2 Imports: stats, graphics, utils, methods Suggests: testthat, knitr, rmarkdown Published: 2024-05-16 DOI: 10.32614/CRAN.package.ROCit Author: Md Riaz Ahmed Khan [aut, cre], Thomas Brandenburger [aut] Maintainer: Md Riaz Ahmed Khan <mdriazahmed.khan at jacks.sdstate.edu> License: GPL-3 NeedsCompilation: no Language: en-US Materials: README NEWS CRAN checks: ROCit results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=ROCit to link to this page.
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