I have plot ROC for several models. These models were used to classify my samples into 2 classes.
Using these commands, I can obtain sensitivity vs. specificity plots for each model:
perf <- performance(pred, "sens", "spec") plot(perf)
Should I rely on the area under the curve (AUC) for each model to conclude which model is better? Other than AUC, should we consider other results so as to conclude which model is better?
If yes, how to get AUC with R? Am I right in assuming that "the smaller it is the better is the classification power of the model?"