Question: In a concise essay, explain why a large performance gap between the Eyeball and Blackbox dev sets indicates overfitting, and why acquiring more data for the Eyeball dev set can remedy this problem.

Sample answer: A large performance gap arises because the Eyeball dev set is repeatedly examined manually, and decisions are tuned to fix errors specific to those particular examples. This makes the model or the error-analysis process fit closely to the quirks of that small set rather than generalizing. Because the Blackbox dev set is not manually inspected, it reflects true generalization performance. Acquiring more data for the Eyeball dev set dilutes the influence of any single overfit example, giving a more representative sample and reducing the risk that tuning decisions are driven by idiosyncrasies of a small set.

Key points:

• Repeated manual analysis on the Eyeball dev set causes overfitting
• The Blackbox dev set is not manually examined and reveals true performance
• A large gap between the two sets signals overfitting
• Acquiring more data reduces the influence of individual examples
• More data yields a more representative, generalizable Eyeball dev set

Rubric: Full credit requires explaining the cause of the gap (repeated manual tuning on a small set), the role of the Blackbox dev set as an unbiased check, and why more data reduces overfitting risk.