# β Quiz M2.02ΒΆ

Question

A model is overfitting when:

a) both the train and test errors are high

b) train error is low but test error is high

c) train error is high but the test error is low

d) both train and test errors are low

Question

Assuming that we have a dataset with little noise, a model is underfitting when:

a) both the train and test errors are high

b) train error is low but test error is high

c) train error is high but the test error is low

d) both train and test errors are low

Question

For a fixed training set, if we change a model parameter to give the model more flexibility, are we more likely to observe:

a) a wider difference between train and test errors

b) a reduction in the difference between train and test errors

c) an increase in the train error

d) a decrease in the train error

Question

For a fixed choice of model parameters, if we increase the number of labeled observations in the training set, are we more likely to observe:

a) a wider difference between train and test errors

b) a reduction in the difference between train and test errors

c) an increase in the train error

d) a decrease in the train error

Question

Polynomial models with a high degree parameter:

a) always have the best test error (but can be slow to train)

b) underfit more than linear regression models

c) get lower training error than lower degree polynomial models

d) are more likely to overfit than lower degree polynomial models

Question

One can always reach zero test error by:

a) choosing the model parameters to find the best overfitting/underfitting tradeoff

b) day-dreaming ;)