Case context: A team is building an image classifier. They currently use hand-designed computer vision features followed by a simple linear classifier, and they are considering merging their user-uploaded images with a large set of internet images to increase training data size.

Question: Based on the source's discussion of algorithm flexibility and merging risk, what should this team diagnose about their situation, and what does the source suggest about the risk they face?

Sample answer: The team should diagnose that their algorithm, hand-designed computer vision features with a simple linear classifier, is an earlier, less flexible type of learning algorithm. According to the source, this type of algorithm carries a real risk that merging user-uploaded images with internet images could worsen performance, so the team should weigh this risk carefully before merging the data sources.

Key points:

• The team's algorithm matches the earlier, less flexible type described in the source
• This type of algorithm carries a real risk of worsening performance from merging data
• The risk is tied to the specific algorithm, not merging data sources in general
• A more flexible algorithm, like a large neural network, would have greatly diminished risk in the same situation

Rubric: Full credit identifies the team's algorithm as the earlier, less flexible type described in the source and correctly concludes that merging carries a real risk of worsening performance for this algorithm.