Question: Contrast the insights gained from examining a full learning curve (plotting both training and dev error) versus measuring error only at the rightmost point. Why is the full curve preferred?

Sample answer: Measuring error at only the rightmost point provides a static snapshot of performance corresponding strictly to using all currently available training data. This gives no insight into trends or data scaling. By plotting the full learning curve with both training and dev error across different dataset sizes, we gain a comprehensive picture of how the algorithm's performance changes as data increases. Viewing both curves on the same plot allows us to more confidently extrapolate the dev error curve to predict whether gathering more data will be beneficial.

Key points:

• The rightmost point only shows performance for all currently available data.
• The full curve gives a comprehensive picture across different training set sizes.
• Plotting both curves together helps in confidently extrapolating the dev error curve.

Rubric: A strong response should state that the rightmost point only shows performance on maximum available data, while the full curve reveals performance across different sizes. It must mention that plotting both curves aids in extrapolating the dev error curve.