If the Bayes decision boundary is linear, we expect QDA to perform better on the training set because it's higher flexiblity will yield a closer fit. On the test set, we expect LDA to perform better than QDA because QDA could overfit the linearity of the Bayes decision boundary.
If the Bayes decision bounary is non-linear, we expect QDA to perform better both on the training and test sets.
We expect the test prediction accuracy of QDA relative to LDA to improve, in general, as the the sample size \( n \) increases because a more flexibile method will yield a better fit as more samples can be fit and variance is offset by the larger sample sizes.
False. With fewer sample points, the variance from using a more flexible method, such as QDA, would lead to overfit, yielding a higher test rate than LDA.