Case Study $2$: Suppose that you have built a classifier that can identify whether an email is spam or not spam. After applying the classifier to the training data, you get the following confusion matrix. $($Marks: $20)$

$-$ Calculate the accuracy, true positive rate, true negative rate, precision, and recall. $(4$ marks$)$

$-$ Based on the accuracy value, do you think the classifier is doing a good job identifying spam $-$ emails? Justify your answer. $(3$ marks$)$

$-$ What is the class imbalance problem? How it is affecting the accuracy for the given scenario. $(3$ marks$)$