Problem 2) Confusion matrix and metrics for measuring the performance a model: Assume that a classification model is trained to detect fraudulence emails from non-fraudulence ones, and we would like to test the classifier's efficacy in making such detections. A test data containing 40 email instances are provided. The labels of the test data are given and denoted as the gold- standard labels. Table below provides the gold-standard labels and the predicted labels using this classifier for each data sample. O means non-fraudulence emails and 1 indicates fraudulence emails. Using this table, answer the following questions. instanse gold-standard labels model-predicted labels ----------------------- - - - - Inasala lo ----------- 37 38 39 40 a) Calculate and show the confusion matrix. b) Calculate and provide the accuracy of the model. c) Calculate the sensitivity of the model. d) Calculate the specificity of the model. e) Calculate F1 Measure of the model. f) Calculate the recall and precision