A Na$$ve Bayes classification model was developed to detect credit card fraud of their

customers. Table $4\mathrm{.}1$ shows the prediction made by the model on the test data. The value

"Yes' indicates a fraudulent transaction made on the credit card and the value $\text{'}$No$\text{'}$

indicates a normal transaction. Table $4\mathrm{.}1$

$($a$)$ Calculate the baseline performance.

$($b$)$ Calculate true positives $($TPs$),$ true negatives $($TNs$),$ false positive $($FPs$)$ and false negatives $($FNs$).$

$($c$)$ Using the computed results in Q$4($b$),$ calculate the following evaluation measures. Round your answer to the nearest $2$ decimal points.

$($i$)$ Classification accuracy rate.

$($ii$)$ Precision.

$($iii$)$ Recall.

$($iv$)$ Specificity.

$($v$)F_1$ score.

$($d$)$ Using the computed results in Q$4($a$)$ and Q$4($c$),$ justify whether you would

propose the model to the company.

$($e$)$ Suggest TWO $(2)$ ways to overcome issues you have observed in Q$4($d$).$