1. Consider the following Training Data Set:
Apply the Naïve 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 Naïve Bayesian Classifier
3. What gives the Naïve Bayesian Classifier the advantage of being computationally inexpensive?
4. Why should we use log-likelihoods rather than pure probability values in the Naïve 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 Naïve Bayesian assumption? • Is the Naïve Bayesian assumption satisfied for this problem? Your Thoughts?
