Case context: A team is building a flower classifier and wants to train on a large archive of botanical field photos together with a smaller set of photos taken by customers on mobile phones. Both collections use the same label set, such as rose, tulip, and daisy.

Question: According to the shared label mapping idea, what additional issue must the team examine before merging the datasets?

Sample answer: It is not enough that both collections use the same labels. The team must check whether one input-to-label rule works well for both sources at the same time. In other words, a model should be able to predict the correct flower label from either field photos or phone photos without needing to know which source the image came from. If the field photos and phone photos differ so much in framing, lighting, or background that no single rule performs well on both, then simply combining the datasets is not justified.

Key points:

• Matching label names alone does not prove the datasets should be merged.
• The important question is whether one shared input-to-label mapping works across both sources.
• The prediction rule should succeed for both kinds of images without source information.
• Strong differences in image style can break the shared mapping assumption.

Rubric: Full credit for saying that matching categories is not enough and that the team must verify a shared input-to-label mapping that works for both sources without source identity. Partial credit for only mentioning that the labels match or only stating that the sources may differ.