Question:
At its most basic level, what specific question about a machine learning pipeline does error analysis by parts help a developer answer?

Sample answer:
Error analysis by parts helps a developer answer which specific component or components of a machine learning pipeline are worth the greatest effort to improve by attributing system mistakes to those individual parts.

Key points:
- It tells us what component(s) performance is worth the greatest effort to improve.
- It attributes errors to specific parts of the pipeline to help prioritize work.

Rubric:
The response should explicitly state that the analysis identifies the component(s) needing the most improvement effort.

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Error analysis by parts identifies which component performances are worth the greatest improvement effort. It can also be done informally by comparing each pipeline component and the overall system against human-level performance.

Error Analysis by Parts

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

For a Siamese cat classifier pipeline, the first part detects and crops cats and the second part classifies whether a crop contains a Siamese cat. Error analysis by parts asks which of these components caused a misclassification.

Siamese Cat Pipeline Error Analysis by Parts

If 100 misclassified dev-set images contain 90 errors attributable to the cat detector and only 10 attributable to the breed classifier, the team should focus more attention on improving the cat detector.

Component Error Counts Guide Pipeline Priorities

An informal way to attribute pipeline errors is to inspect the outputs of each part and decide which part made a mistake. This informal method can be sufficient in some cases.

Informal Pipeline Error Attribution

Pipeline error attribution can be ambiguous when an upstream component produces a poor output but a sufficiently skilled downstream component might still have handled it correctly. If such ambiguous cases are rare, the exact attribution choice may not change the result much.

Ambiguous Pipeline Error Attribution Cases

For a pipeline with steps A, B, and C, test each dev-set mistake by manually replacing A with a perfect output and running the rest of the pipeline. If the system becomes correct, attribute the error to A; otherwise repeat with B, and if that still does not fix the output, attribute the error to C.

General Error Attribution Procedure for Multi-Step Pipelines

A pipeline can be debugged informally by comparing each component and the overall system to human-level performance. For path planning, the human comparison should use the same inputs as the component, such as only the earlier components outputs rather than raw camera images.

Comparing Pipeline Components to Human-Level Performance

What does error analysis by parts primarily tell you about a machine learning pipeline?

Error analysis by parts can only be performed using a rigorous formal procedure, not informally.

Error analysis by parts tells us what component(s) performance is worth the greatest _____ to improve.

Match each error analysis by parts concept to its correct description from Machine Learning Yearning.

Order the steps of the informal error analysis procedure Ng describes for a self-driving car pipeline.

Which three components make up the self-driving car pipeline Ng uses to illustrate informal error analysis by parts?

The primary goal of error analysis by parts is to help a developer decide which pipeline component to prioritize for improvement.

By carrying out error analysis by parts, you can _____ each mistake the algorithm makes to one or more pipeline components.

Match each component in Ng's self-driving car pipeline to the output it produces.

Order the reasoning steps a developer follows when applying error analysis by parts to prioritize pipeline improvements.

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).

Explain the core purpose and informal execution of error analysis by parts.

Case context:
You are working on a self-driving car application with a pipeline consisting of a car detection algorithm, a pedestrian detection algorithm, and a path planning module. You want to debug the pipeline and improve the overall system performance without following a rigorous formal procedure.

Question:
How should you informally apply error analysis by parts to decide which component of the autonomous driving pipeline to prioritize for improvement? What specific comparisons should you make?

Sample answer:
To informally apply error analysis by parts, I should ask how far each individual component is from human-level performance. Specifically, I need to compare the car detection algorithm's performance to human-level car detection, and the pedestrian detection algorithm's performance to human-level pedestrian detection. Finally, I should evaluate how far the overall self-driving system's performance is from human-level driving performance. By making these comparisons, I can identify which component is furthest from human capability and prioritize improving it.

Key points:
- Compare the car detection component to human-level performance at detecting cars.
- Compare the pedestrian detection component to human-level performance at detecting pedestrians.
- Compare the overall self-driving system's performance to human-level performance.
- Use these comparisons to determine which component requires the most effort to improve.

Rubric:
The answer must identify the specific informal questions to ask, focusing on comparing the individual components (car and pedestrian detection) and the overall system to human-level performance.

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