Package: ROCit 2.1.1
ROCit: Performance Assessment of Binary Classifier with Visualization
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.
Authors:
ROCit_2.1.1.tar.gz
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ROCit_2.1.1.tgz(r-4.4-any)ROCit_2.1.1.tgz(r-4.3-any)
ROCit_2.1.1.tar.gz(r-4.5-noble)ROCit_2.1.1.tar.gz(r-4.4-noble)
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ROCit.pdf |ROCit.html✨
ROCit/json (API)
NEWS
| # Install 'ROCit' in R: |
| install.packages('ROCit', repos = c('https://riazakhan94.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/riazakhan94/rocit/issues
Last updated 2 years agofrom:068953e58f. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 07 2024 |
| R-4.5-win | NOTE | Sep 07 2024 |
| R-4.5-linux | NOTE | Sep 07 2024 |
| R-4.4-win | NOTE | Sep 07 2024 |
| R-4.4-mac | NOTE | Sep 07 2024 |
| R-4.3-win | OK | Sep 07 2024 |
| R-4.3-mac | OK | Sep 07 2024 |
Exports:cartesian_2DciAUCciROCciROCbinciROCempconvertclassgainstablegetsurvivalgettptnfpfninvlogitksplotlogitmeasureitMLestimatesrankorderdatarocittrapezoidarea
Dependencies:
