5

4. Classifiers (14%] Given the training data in the below table. Home Owner Job Experience (1-5) Defaulted No 2 Yes Yes 1 Yes No 3 Yes Yes 2 No No 1 No Given Bob (Home Owner=No, Job Experience = 1) 1) Using Naive Bayes Classifier to predict if Bob will be defaulted or not 2) Build a Decision Tree Classifier to predict Bob 3) Build a KNN Classifier (k = 3) to predict Bob 4) Run k-Means (with k = 2) on the whole training data using all attributes (Home Owner, Job Experience, and Defaulted) to see what two clusters would be produced. You only need to run the algorithm with 2 iterations 5) What are the main differences between supervised learning and unsupervised learning, and what are their roles in real-world applications