Question: Based on Andrew Ng's guidelines, explain the primary goal of performing error analysis by parts on a complex machine learning pipeline. Additionally, describe how this analysis can be conducted informally using the concept of human-level performance.

Sample answer: The primary goal of error analysis by parts is to identify which specific component of a complex machine learning pipeline is worth the greatest effort to improve. By attributing errors to individual components, developers can effectively prioritize their work. Informally, this analysis can be conducted by comparing the performance of each individual pipeline component (such as a car detector or a pedestrian detector) against human-level performance for that specific task, and then also comparing the overall system's performance to human-level performance. This comparison highlights which components are lagging furthest behind human capability and thus offer the most room for improvement.

Key points:

• The goal is to determine which pipeline components are worth the greatest improvement effort.
• Errors are attributed to specific parts of the pipeline to prioritize work.
• Informal error analysis involves comparing each component to human-level performance.
• The overall system's performance is also compared to human-level performance.

Rubric: The answer should state the main goal (prioritizing improvement efforts by attributing errors to components) and explain the informal method (comparing individual components and the overall system to human-level performance).