Case context: A team trains a classifier on 18,500 examples. The classes are close to evenly represented, and the plotted learning curves already show a clear pattern.

Question: Should the team average results across several random samples or build class-balanced samples? Diagnose the situation and justify your decision.

Sample answer: The team probably should not use either approach. The curves are already easy to interpret, so there is no strong evidence that noisy measurements are hiding the main trend. In addition, the dataset is large and the class mix is fairly even, so the usual reasons for these noise-reduction steps do not apply.

Key points:

• The learning curves already make the trend visible.
• Noise-reduction steps are mainly useful when the curves are too irregular to interpret.
• The training set is large.
• The class balance is roughly even.
• The two techniques are probably unnecessary.

Rubric: The response should advise against adding the techniques, explain that the current curves are already readable, and connect the large dataset and near-balanced classes to why the extra steps are not needed.