Indicate which of answer(s) in parentheses are correct for each question: 

a. For binary class classification, does logistic regression always produce a linear decision boundary? (Yes; No) 

b. You train a linear classifier on 1,000 training points and discover that the training accuracy is only 50%. Which of the following, if done in isolation, has a good chance of improving your training accuracy? (Add new features; Train on more data; Train on less data) 

c. Does there exist data that is separable with a Gaussian RBF kernel but not with a quadratic kernel? (Yes; No)

d. Does there exist data that is separable with a linear kernel but not with a quadratic kernel? (Yes; No) 

e. You are training a logistic regression model and you find that your training loss is near 0 but test loss is very high. Which of the following is expected to help to reduce test loss? (Increase the training data size; Decrease the training data size; Increase model complexity; Decrease model complexity; Train on a combination of your training data and your test data but test only on your test data; Conclude that Machine Learning does not work)