✅ Quiz M2.02

✅ 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

Select a single answer

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

Select a single answer

Question

For a fixed training set, by sequentially adding parameters to give more flexibility to the model, we are 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 increased or steady train error

  • d) a decrease in the train error

Select all answers that apply

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 increased or steady train error

  • d) a decrease in the train error

Select all answers that apply

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

Select all answers that apply

Question

If we chose the parameters of a model to get the best overfitting/underfitting tradeoff, we will always get a zero test error.

  • a) True

  • b) False

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