Case Study

Choosing the most important error type to fix in a document classifier

Case context: You are building a classifier for scanned office documents. You review 120 development examples that your system misclassified. In the manual analysis, 48% of these errors are invoices, while 7% are handwritten notes.

Question: Based on the manual review of these 120 examples, which error type should you fix first, and how does the analysis support that choice?

Sample answer: You should prioritize invoice misclassifications. They make up 48% of the reviewed mistakes, compared with only 7% for handwritten notes. Fixing the most common error category is likely to reduce the overall development error the most.

Key points:

  • Identify invoices as the highest-priority error category
  • Use the 48% versus 7% counts from the review
  • Explain that the largest category offers the biggest opportunity to lower total error

Rubric: The answer must identify invoices as the priority category and justify this choice by noting that they account for a much larger share (48% of the 120 misclassified examples) than handwritten notes (7%).

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Updated 2026-08-12

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