Case context: A team plots learning curves using a training set of more than 10,000 examples. The class distribution is not very skewed, and the underlying trends are already visible.

Question: Should the team average over multiple random subsets or construct balanced subsets? Diagnose the situation and justify the decision.

Sample answer: The team probably should not use either technique. Its initial learning curves already reveal the underlying trends, so excessive noise has not created the problem these methods address. The large training set and class distribution that is not very skewed provide further reason to expect that the techniques are unnecessary.

Key points:

• The underlying trends are already visible.
• Noise-reduction techniques are justified only after excessive noise is observed.
• The training set exceeds 10,000 examples.
• The class distribution is not very skewed.
• The techniques probably are unnecessary.

Rubric: The response should recommend against adding the techniques, cite the readability of the existing curves as the primary reason, and connect the large training set and non-skewed distribution to the source guidance.