Examine the differences between LDA and QDA.

Explain with your own words the following

A) 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) f 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) 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) 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 answer.