The following table contains the outcome of classifying 10 testing tuples using a probabilistic classifier. For each tuple, the actual class (P or N) is given in the second column, and the probability (of class P) returned by the classifier is in the third column. (a) For each row of the table, assume that the threshold of probability for predicting class P is the probability in that row (see Example 8.11 on page 375 of the textbook for further description), determine the numbers of true positives (TP), false positives (FP), true negatives (TN), false negatives (FN) the true positive rate (TPR) and false positive rate (FPR) of the entire set of tuples. (b) Plot the ROC curve for the data. The following table contains the outcome of classifying 10 testing tuples using a probabilistic classifier. For each tuple, the actual class (P or N) is given in the second column, and the probability (of class P) returned by the classifier is in the third column. (a) For each row of the table, assume that the threshold of probability for predicting class P is the probability in that row (see Example 8.11 on page 375 of the textbook for further description), determine the numbers of true positives (TP), false positives (FP), true negatives (TN), false negatives (FN) the true positive rate (TPR) and false positive rate (FPR) of the entire set of tuples. (b) Plot the ROC curve for the data