Check your understanding of this module before moving on.
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Checkpoint quiz 3 questions
A quick check on this module's key ideas before you move on
Question 01
Why does RMSE penalize a single large error more than MAE does?
B is correct. MAE weighs every unit of error equally. RMSE squares each error first, so an error twice as large contributes four times as much, not just twice.
Question 02
If RMSE is much larger than MAE on the same dataset, what does that most likely indicate?
B is correct. A wide gap between RMSE and MAE is itself a diagnostic signal: a few large mistakes are inflating the squared-error metric while most predictions are actually fine.
Question 03
Why does R² mechanically increase, or stay the same, when you add any additional feature to a linear regression model, even pure noise?
B is correct. Extra flexibility means the optimizer can always find at least a tiny accidental pattern in the training sample to exploit, which can never make the fit worse on the data it was trained on. That's exactly why adjusted R² exists.