Problem 1 (20 points) Consider the following confusion matrix. predicted class C1 (positive) C2 (negative) actual class C1 (positive) 28 72 C2 (negative) 328 781 Compute sensitivity, specificity, precision, accuracy, F-meassure, F2, and MCC measures. You have to show all your calculations. Problem 2 (20 points) Suppose you built two classifier models MI and M2 from the same training dataset and tested them on the same test dataset using 10-fold cross-validation. The error rates obtained over 10 iterations (in each iteration the same training and test partitions were used for both MI and M2) are given in the table below. Determine whether there is a significant difference between the two models using the statistical method discussed that we discussed in the class (this method is also discussed in Section 8.5.5, pp 372-373 of the textbook). Use a significance level of 1%. If there is a significant difference, which one is better? Iteration |M1 M2 0.13 0.19 2 0.12 0.1 3 0.09 0.12 4 0.15 0.1 5 0.03 0.07 6 0.07 0.05 7 0.2 0.1 8 0.14 0.11 0.12 0.07 10 0.14 0.11 Note: When you calculate var(MI - M2), calculate a sample variance (not a population variance). You must show all calculations, including the calculation of the test statistic.Problem 3 (10 points). The following table shows a test result of a classifier on a dataset. Tuple id Actual Class Probability 1 P 0.72 2 N 0.70 3 N 0.87 4 P 0.92 5 P 0.75 6 0.89 7 N 0.82 8 P 0.73 9 N 0.91 10 P 0.96 Problem 3-1. For each row, compute TP, FP, TN, FN, TPR, and FPR. Problem 3-2. Plot the ROC curve for the dataset. You must draw the curve yourself (i.e., don't use Weka, R, or other software to generate the curve)