r-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 classifiers for certain thresholds. 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. The ROCit package provides flexibility to easily evaluate threshold-bound metrics.
- Versions: 2.1.2
- Website: https://cran.r-project.org/package=ROCit
- Licenses: GPL 3
- Package source: gnu/packages/cran.scm
- Builds: See build status
- Issues: See known issues
Installation
Install the latest version of r-rocit as follows:
guix install r-rocit
Or install a particular version:
guix install r-rocit@2.1.2
You can also install packages in augmented, pure or containerized environments for development or simply to try them out without polluting your user profile. See the guix shell documentation for more information.
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