Question 1 (1 point) Suppose you have two binary classification datasets: Dataset A has m binary features and Dataset B has m continuous (i.e., real-valued) features. You plan to run "Bernoulli Naive Bayes" (i.e., Naive Bayes with binary features) on Dataset A and Gaussian Naive Bayes on Dataset B. Which dataset/model requires more parameters to learn? Gaussian Naive Bayes requires more parameters Binary Naive Bayes requires more parameters They require the same number of parameters Not enough information