How to use of the roc.plot() function In R Language
To plot the ROC-AUC curve for a model, we can use another library called verification in R programming. To use the function, we must first install and import the verification library into our environment.
After that, we plot the data using the roc.plot() function to get a clear picture of the ‘Sensitivity’ and ‘Specificity’ of the data values, as shown below.
R
install.packages("starter")library(verification)x<- c(0,0,0,1,0,0)y<- c(.7, .7, 0, 1,5,.6)gfgData<-gfgData.frame(x,y)names(data)<-c("plot","axis")roc.plot(gfgData$yes, gfgData$no) |
Plotting ROC curve in R Programming
Error metrics allow us to evaluate and justify the model’s performance on a specific dataset. One such error metric is the ROC plot. A classification error metric is the ROC plot, also known as the ROC AUC curve. That is, it assesses the performance and outcomes of classification machine learning algorithms.
To be more specific, the ROC curve represents the value’s probability curve, whereas the AUC is a measure of the separability of different groups of values/labels. The ROC AUC curve can be used to analyze and draw conclusions about how many values have been correctly distinguished and classified by the model based on the labels.
The higher the AUC score, the better the prediction of the predicted values. In technical terms, the ROC curve is the relationship between a model’s True Positive Rate and False Positive Rate. Let us now try to apply the concept of the ROC curve in the following section.