v$.$ Using examples and mathematical equations indicate the difference between Entropy

and Gini Impurity in a Decision Tree?

You are working on a binary classification problem to predict whether patients have Covid$-19$

disease $($Positive$)$ or not $($Negative$)$ using a machine learning model. After training your

model, you obtain the following confusion matrix on the test data:

vi$.$ Calculate the following performance metrics: accuracy, precision, recall, F$1-$score,

and specificity and interpret these metrics in the context of the Covid$-19$ disease

prediction problem.

vii. Discuss the implications of changing the decision threshold of your model. How

would increasing or decreasing the threshold affect the confusion matrix and the

derived metrics.

viii. Which type of error $($false positive or false negative$)$ do you think is more critical to

minimize, and why? Propose a strategy to mitigate this type of error.

$[5]$

ix$.$ If the prevalence of the disease is very low $($minority$)$ and you suspect the dataset is

imbalanced. Describe five steps you can utilize to handle this imbalance.