| Name : R-ROCit
| |
| Version : 2.1.2
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp154.2.1
| Date : 2024-06-04 22:02:54
|
| Group : Development/Libraries/Other
| Source RPM : R-ROCit-2.1.2-lp154.2.1.src.rpm
|
| Size : 0.40 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Performance Assessment of Binary Classifier with Visualization
|
Description :
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.4/x86_64 |
