# ✅ Quiz M2.02
```{admonition} 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
```
+++
```{admonition} 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
```
+++
```{admonition} 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
```
+++
```{admonition} 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
```
+++
```{admonition} 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
```
+++
```{admonition} Question
One can always reach zero test error by:
- a) choosing the model parameters to find the best overfitting/underfitting tradeoff
- b) day-dreaming ;)
```