Question: Explain why looking through roughly 100 training examples that a model handles badly can reveal the main kinds of training-set mistakes, and how this resembles error analysis on a development set.

Sample answer: Reviewing about 100 low-performing training examples gives a direct view of what is going wrong, instead of only seeing a single overall loss or accuracy number. By sorting those cases into a few error categories, a practitioner can see which problems occur most often and which data issues deserve attention first. This is the same basic idea used in development-set error analysis: classify mistakes by type, count how often each type appears, and use those counts to decide what to fix.

Key points:

• Inspect roughly 100 examples the model gets wrong
• Look for recurring categories of training-set errors
• Count examples in each category to find the biggest problems
• The method is analogous to dev-set error analysis

Rubric: Full credit requires mentioning: (1) reviewing about 100 examples, (2) identifying major categories of errors, and (3) the link to counting errors in a way similar to dev-set analysis.