QUESTION $150$ Marks

i$.$ Explain in details, the stages of the Machine Learning life cycle. $[5]$

ii$.$ Explain in detail the difference between overfitting and underfitting in Machine

Learning and ways to overcome them. $[10]$

iii. What is the difference between regression and classification $[5]$

iv$.$ Write a pseudo algorithm for the K$-$means clustering. $[5]$

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

and Gini Impurity in a Decision Tree? $[5]$

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:

Predicted Positive Predicted Negative

Actual Positive $4010$

Actual Negative $2030$

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. $[5]$

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. $[5]$

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. $[5]$