The correct answer is: B. discrete
A multinomial naive Bayes classifier is a probabilistic machine learning model that is used for classification tasks. It is a type of naive Bayes classifier, which is a simple probabilistic classifier based on Bayes’ theorem. The multinomial naive Bayes classifier assumes that the features are independent of each other, given the class label. This assumption is often violated in practice, but the multinomial naive Bayes classifier can still be effective in many cases.
The multinomial naive Bayes classifier is a discrete distribution because it predicts the probability of a class label given a set of features. The features are discrete because they can take on a finite number of values. The class labels are also discrete because they can take on a finite number of values.
The other options are incorrect because they are not discrete distributions. A continuous distribution is a probability distribution that can take on any value in an interval. A binary distribution is a probability distribution that can take on only two values, 0 and 1.