Bayesian Networks and Nave Bayes Classifiers

(a) Given a training dataset including 30 instances and a Bayesian network indicating the relationships between 3 features (i.e. Income, Student and Credit Rate), and the class attribute (i.e. Buy Computer), please create the conditional probability tables by hand.

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

(c) Based on the conditional independence assumption between features, please create the conditional probability tables by hand.

(d) Make predictions for 2 testing instances by using the nave Bayes classifier.

Buy Computer Training Instances Instance 1 Instance 2 Credit Rating Buy Computer Fair Excellent Testing Instances Instance_31 Instance_32 Income Student Low False High False Credit Rating Excellent Fair Instance 3 Fair Instance 4 Fair Excellent Fair Instance_5 Instance_6 Instance-7 Instance & Excellent Fair Instance_9 Excellent Instance 10 Excellent Income Student Instance_11 Fair Fair Fair Instance 12 Instance.13 Instance 14 Instance.15 Excellent Income Student High True Low False Low True High False Low True High False High True Low True Low False Low True High True Low False Low True High False Low True High False High True Low True Low False Low True High False Low True High False High True Low False Low True Low True Low True Low False High True Fair Instance 16 Excellent Excellent Fair Instance.17 Instance.18 Instance.19 Instance 20 Instance 21 Credit Rating Buy Computer Excellent Excellent Excellent Excellent Instance 21 Instance_23 Excellent Instance 24 Fair Fair Fair Instance 25 Instance_26 Instance 27 Instance_28 Instance 29 Instance_30 Fair Fair Fair Excellent