I’m trying to study for my Computer Science course and I need some help to understand this question.
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) Show your work
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?