1. Consider the following Training Data Set: 
Apply the Na¯ve Bayesian Classifier to this data set and compute the probability score for P (y = 1|X) for X = (1,0,0) 
2. List some prominent use cases of the Na¯ve Bayesian Classifier
3. What gives the Na¯ve Bayesian Classifier the advantage of being computationally inexpensive?
4. Why should we use log-likelihoods rather than pure probability values in the Na¯ve Bayesian Classifier?
5. What is a confusion matrix and how it is used to evaluate the effectiveness of the model?
6. Consider the following data set with two input features temperature and season ¢ What is the Na¯ve Bayesian assumption? ¢ Is the Na¯ve Bayesian assumption satisfied for this problem? Your Thoughts?