Q3 [10 pts] Discriminant Analysis We now examine the differences between LDA and QDAA (a) [2 pts] If the Bayes decision boundary is linear, do we expect LDA or QDA to perform better on the training set? On the test set? (b) [2 pts] If the Bayes decision boundary is non-linear, do we expect LDA or QDA to perform better on the training set? On the test set? (c) [3 pts] In general, as the sample size n increases, do we expect the test prediction accuracy of QDA relative to LDA to improve, decline, or be unchanged? Why? (d) [3 pts] True or False: even if the Bayes decision boundary for a given problem is linear, we will probably achieve a superior test error rate using QDA rather than LDA because QDA is flexible enough to model a linear decision boundary. Justify your