The following two confusion matrices represent the performance of two different classifiers, C1 and C2, on the same validation dataset (which had 100 data points). Both classifiers were built to predict whether the person is likely to buy a luxury car.

Compare the two classifiers based on their predictive accuracy as well as precision, recall, and F-measure (for class \Yes", i.e., for the purchase outcome). Show the calculation for each metric (i.e., dont just report which classifier has higher performance).

Also, compute the accuracy of the naive (majority) rule on this validation dataset.

Hint: you may want to first draw the confusion matrix that you would get with naive/majority rule, to help you with accuracy calculation.

Classifier C1: Classifier C2: Predicted Yes No Actual Yes No 20 8 12 60 Predicted Yes No Actual Yes No 25 18 7 50