$4.($a$)$ Fit at least one other binary classifier $($e$.$g$.,$ a linear probability model or a Support Vector

Machine classifier$)$ to the dataset. Describe its performance relative to the classifiers

highlighted above.

$($b$)$ Is your training dataset balanced? Comment on the drawbacks of fitting a Statistical

Learning technique on an unbalanced dataset. Can confusion matrix be a useful performance

metric for this problem? Can you think of $/$ identify a technique to address this concern? If so$,$

why do you think that the method$($s$)$ could work?

Hint: This question has not been discussed by way of a formal teaching section on the module.

It is up to each student group to search for a systematic understanding and solution to the

phenomenon of imbalanced data. $[9$ marks$]$ The data consists of variables that inform the credit worthiness of a bank customer. The

dependent variable, Class, is binary and differentiates customers, on their observed credit

performance, as either Good or Bad.

The independent variables consists of: checking account status, duration, credit history,

purpose of the loan, amount of the loan, savings accounts or bonds, employment duration,Instalment rate in percentage of disposable income, personal information, other

debtors$/$guarantors$,$ residence duration, property, age, otherinstalment plans, housing, numberInstalment rate in percentage of disposable income, personal information, other

debtors$/$guarantors$,$ residence duration, property, age, otherinstalment plans, housing, number