(4) Nave Bayes classifier and model evaluation (11 marks) (a) Explain a method to handle zero counts and a method to handle numerical underflow in the Nave Bayes classifier. Use examples to support your answers. (5 marks) (b) Why the Nave Bayes classifier is efficient (e.g., compared with Decision Tree)? (2 marks) (c) Considering the following confusion matrix, what are the TP, FP, TN, FN, precision, recall and F. measure? (4 marks) buy_computer = buy_computer = yes no Total Actual class Predicted class buy_computer Eyes buy_computer = no Total 6954 46 412 2588 7000 3000 10000 7366 2634 (5) MapReduce and Spark Model (8 marks) (a) Use an example to explain how the MapReduce model can process a "word count" problem. (3 marks) (b) Explain how the MapReduce model can process the relational-algebra operation "selection". Use a concrete example to support your answer. (3 marks) (c) Explain how the Spark model extends the MapReduce model. (2 mark)