Case context: A binary classification dataset contains many more negative than positive examples. A learning curve built from increasing random subset sizes shows an unexpected fluctuation at 10 examples. Inspection reveals that the 10-example subset contains only negative examples.

Question: What should the learner diagnose as the likely reason for the fluctuation, and why would this subset make meaningful learning difficult?

Sample answer: The learner should diagnose the 10-example subset as unrepresentative because the skewed class distribution allowed the random sample to contain only negative examples. With no positive examples represented, the algorithm has little opportunity to learn something meaningful about distinguishing the two classes, so the corresponding training and dev errors may fluctuate unexpectedly.

Key points:

• The subset is unrepresentative
• Class skew makes such a subset more likely
• The subset contains no positive examples
• Missing positive examples makes meaningful learning difficult
• The noisy point can result from random subset composition

Rubric: Full credit requires identifying class skew and random selection as the cause of the unrepresentative subset, noting the absence of positive examples, and connecting that absence to both difficult learning and a noisy curve value.