W ! 3. a) Write the differences between generative and discriminative models of probabilistic classifiers. Write the algorithm of Nave Bayes classifier for discrete valued feature. c) Consider a set of training examples given in Table 1 to train a robot, 'RecycleBot' to predict whether or not an office contains a recycling bin. Table 1: Training dataset Status Floor Department Office size Recycle bin 1. Faculty Four CSE Medium Yes 2. Student Four EEE Large Yes Staff Five CSE Medium No Student Three EEE Small Yes 5. Staff Four CSE Medium No Construct a Bayesian network that represents all attributes in the 'RecycleBot example, assuming that the predicting attributes are pairwise independent. Provide the probability table for each of the predicting attributes. Show how a Nave Bayesian classifier would classify the following instance: Status='Student', Floor='Four', Department='CSE, Office size='Small', Recycle bin=? 5