Question: For binary image classification problems, explain how the difficulty of the task changes the amount of training data needed. Include at least two examples that differ in difficulty.

Sample answer: As a task becomes harder, it usually takes more training data to reach the same performance level. Simple binary classification tasks can often be learned from a relatively small dataset, while tasks that require subtle distinctions need much more data. For example, deciding whether a document photo is tilted is a fairly easy binary task and may be learned with limited examples. By contrast, deciding whether a close-up image shows a hairline crack in a ceramic tile is harder because the relevant visual cue is small and easy to miss, so the model generally needs many more labeled images.

Key points:

• Simpler tasks need fewer training examples.
• Harder tasks need substantially more data.
• The answer should give at least two binary image classification examples with different difficulty levels.

Rubric: The essay should clearly explain the link between task difficulty and data requirements. It must include at least two binary image classification examples of different difficulty, such as a simple tilt check versus detecting a subtle crack, to illustrate the relationship.