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I have fitted a SVM model and created the ROC curve with ROCR package. How can I compute the Area Under the Curve (AUC)?
set.seed(1)
tune.out=tune(svm ,Negative~.-Positive, data=trainSparse, kernel ="radial",ranges=list(cost=c(0.1,1,10,100,1000),gamma=c(0.5,1,2,3,4) ))
summary(tune.out)
best=tune.out$best.model
##prediction on the test set
ypred = predict(best,testSparse, type = "class")
table(testSparse$Negative,ypred)
###Roc curve
yhat.opt = predict(best,testSparse,decision.values = TRUE)
fitted.opt = attributes(yhat.opt)$decision.values
rocplot(fitted.opt,testSparse ["Negative"], main = "Test Data")##
631
You can divide the space into 2 parts: a triangle and a trapezium. The triangle will have area TPR*FRP/2 , the trapezium (1-FPR)*(1+TPR)/2 = 1/2 - FPR/2 + TPR/2 - TPR*FPR/2 . The total area is 1/2 - FPR/2 + TPR/2 . This is how you can get it, having just 2 points.
AUC: Area Under the ROC Curve This function is a wrapper for functions from the ROCR package.
The roc() function takes the actual and predicted value as an argument and returns a ROC curve object as result. Then, to find the AUC (Area under Curve) of that curve, we use the auc() function. The auc() function takes the roc object as an argument and returns the area under the curve of that roc curve.
Start with the prediction Method from the ROCR Package.
pred_ROCR <- prediction(df$probabilities, df$target)
to get the ROC in a plot:
roc_ROCR <- performance(pred_ROCR, measure = "tpr", x.measure = "fpr")
plot(roc_ROCR, main = "ROC curve", colorize = T)
abline(a = 0, b = 1)
and get the AUC Value:
auc_ROCR <- performance(pred_ROCR, measure = "auc")
auc_ROCR <- [email protected][[1]]
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