Question 1

(a) Given a training dataset including 30 instances and a Bayesian network indicating the relationships between 3 features and the class attribute (i.e. Buy Computer), please compute the conditional dependency tables for individual features.

(b) Make predictions for 2 testing instances by using the Bayesian network classifier

(c) Based on the conditional independence assumption between features, please compute the conditional dependency tables for individual features.

(d) Predict the buying computer behaviour for 2 testing instances by using the naive Bayes classifier

Fair Income Student Credit Rating Buy Computer Fair Training Instances Income Student Credit Rating Buy Computer Instance: 1 High True Yes Instance2 Low False Excellent No Instance 3 Low True Fair No Instance 4 High False Fair No Instance 5 Low True Excellent Yes Instance 6 High False Fair Yes Instance. 7 High True Excellent Yes Instances Low True Fair No Instance 9 Low False Excellent Yes Instance 10 Low True Excellent No Instance 11 High "True Fair No Instance 12 Law False Yes Instance 13 Low "True Fair No Instance.14 High False Excellent No Instance 15 Low True Yes Instance 16 High Fabe Excellent Yes Instance.17 High True Excellent No Instance.18 Low True Fair No Instance 19 Low False Excellent Yes Instance 20 Low True Excellent No Instance 21 False Excellent Yes Instance.21 Low True Excellent Instance 23 High False Excellent No Instance 24 High True Fair No Instance 25 Law False Fair Yes Instance 26 Low True Fair No Instance_27 Law True Fair Yes Instance_28 Law True Fair Yes Instance 29 Low File Fair No Instance 30 High True Excellent No Fair Testing Instance Income Student Credit Rating Yes Instance_31 High False Fair Instance_32 Low False Excellent Fair Income Student Credit Rating Buy Computer Fair Training Instances Income Student Credit Rating Buy Computer Instance: 1 High True Yes Instance2 Low False Excellent No Instance 3 Low True Fair No Instance 4 High False Fair No Instance 5 Low True Excellent Yes Instance 6 High False Fair Yes Instance. 7 High True Excellent Yes Instances Low True Fair No Instance 9 Low False Excellent Yes Instance 10 Low True Excellent No Instance 11 High "True Fair No Instance 12 Law False Yes Instance 13 Low "True Fair No Instance.14 High False Excellent No Instance 15 Low True Yes Instance 16 High Fabe Excellent Yes Instance.17 High True Excellent No Instance.18 Low True Fair No Instance 19 Low False Excellent Yes Instance 20 Low True Excellent No Instance 21 False Excellent Yes Instance.21 Low True Excellent Instance 23 High False Excellent No Instance 24 High True Fair No Instance 25 Law False Fair Yes Instance 26 Low True Fair No Instance_27 Law True Fair Yes Instance_28 Law True Fair Yes Instance 29 Low File Fair No Instance 30 High True Excellent No Fair Testing Instance Income Student Credit Rating Yes Instance_31 High False Fair Instance_32 Low False Excellent