Machine Learning Question

3. Describe how random, max and bayesian classifiers are used for prediction and provide at least one scenario where each classifier can perform as well as the other two. if not possible, state why. 4. Suppose that the probability distribution of a random variable that represent categories is given by P(X-k 1/2k where k is the category. Only the categories with at least 0.03 are included in the computation of naive bayes estimator. The probability of feature i in category j is given by the formula 1/2i +j). Compute the probability that a given observation with 3 features is in category 2 5. Using the same assumptions as the previous part, deduce a general formula for the probability of given n observations is in the category with the index n/2. No need to simplify