Bayesian networks and nave Bayes classifiers.

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

(b) Make predictions for 2 testing observations by using a 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 observations by using a nave Bayes classifier.

Training Observations Income Student Income Student Credit Rating Buy Computer High True Fair No Testing Observations Observation_31 Credit Rating Buy Computer Fair ? Observation_1 High True Observation 2 Low False Excellent No Observation 32 Low False Fair ? Observation 3 Low True Fair No Observation 4 High False Fair No Observation 5 Low True Excellent Yes Observation 6 False High High Low Fair Excellent Yes Yes Observation_7 True Observation 8 True Fair No Income Student Observation_9 Low False Excellent Yes No Low True Excellent Observation 10 Observation 11 High True Fair No Observation_12 Low False Fair No Low True Fair Observation.13 Observation_14 Observation_15 No No 17 High False Excellent Low True Fair Yes False Excellent Yes Observation_16 Observation.17 High High True Excellent No Buy Observation.18 Low Low True False Fair Excellent No Yes Credit Rating Computer Observation_19 Observation 20 Observation_21 Low True No Excellent Excellent False Yes High Low True Excellent Yes Observation_21 Observation_23 False Excellent No High High Low True Fair No Yes False Fair Observation_24 Observation_25 Observation_26 Observation 27 Low True Fair No Low True Fair No Observation_28 Low True Fair Yes Low Fair No Observation_29 Observation 30 False True High Fair Yes