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.
