Q4 Machine Learning Trivia 6 Points Q4.1 1 Point The main difference between classification and regression algorithms is that classifiers assume the input features are discrete, while regressors assume the input features are continuous. True O False Q4.2 1 Point Comparing error rates on training data is the preferred way to choose between multiple classifiers. True O False Q4.3 1 Point The computational cost of training a joint Bayes classifier scales linearly with the number of features O True O False Q4.4 1 Point The computational cost of training a naive Bayes classifier scales linearly with the size of the training data. O True O False Q4.5 1 Point The optimal Bayes error rate can be computed from the training data. O True O False Q4.6 1 Point When using cross-validation to estimate the MSE of a regression algorithm, increasing the number of folds always improves the accuracy of your estimate. O True O False