Question: Describe what a learning curve is in machine learning and explain why it is useful for model diagnosis.

Sample answer: A learning curve is a graph that shows how a model’s validation or development error changes as the number of training examples increases. One axis represents the amount of training data, and the other axis represents error on held-out data. It is useful because it helps practitioners see whether extra data is still improving the model, whether progress has started to flatten out, and whether the model’s behavior suggests a data, bias, or variance problem.

Key points:

• Horizontal axis: number of training examples
• Vertical axis: dev-set or validation error
• Useful for judging how performance changes as training data grows

Rubric: The student must identify the two axes of a learning curve and explain how the graph helps diagnose model behavior.