1. 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 Rate), 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. Testing Observations Income Student Credit Rating Buy Computer Observation 31 Low True Excellent ? Observation-32 High True Fair ? Income Student True Training Observations Income Student Credit Rating Buy Computer Observation_1 High True Fair No Observation 2 Low False Excellent No Observation 3 Low True Fair No Observation 4 High False Fair No Observation 5 Low True Excellent Yes Observation 6 6 False Fair Yes Observation.7 High True Excellent Yes Observation_8 Low True Fair No Observation_9 Low False Excellent Yes Observation. 10 Low True Excellent No Observation 11 High True Fair No Observation.12 Low False Fair No Observation_13 Low Fair No Observation. 14 High False Excellent No Observation.15 Low True Fair Yes Observation.16 High False Excellent Yes Observation.17 High True Excellent No Observation 18 Low True Fair No Observation_19 Low False Excellent Yes Observation_20 Low True Excellent No Observation 21 High False Excellent Yes Observation 21 Low True Excellent Yes Observation_23 High False Excellent No Observation_24 High True Fair No Observation_25 Low False Fair Yes Observation_26 Low True Fair No Observation_27 Low True Fair No Observation 28 Low True Fair Yes Observation_29 Low False Fair No Observation_30 High True Fair Yes Credit Rating Buy Computer