# ✅ Quiz M2.02¶

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 ;)