We have primarily talked about Classification methods, such as, kNN, Naive Bayes, C5.0, RIPPER, CART, Logistic Regression, etc.
In the Classification setting we have used Accuracy/Success Rate to Evaluate the "usefulness" of an algorithm.
\(Accuracy = \frac{TP + TN}{TP + TN + FP + FN}\)
So we have looked at the Confussion Matrix.
acc <- mean( pred == testy )