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The Blackbox dev set is the second subset of a split dev set, consisting of the remaining examples after the Eyeball dev set is drawn; in the running example it contains 4,500 examples. It can be used to evaluate classifiers automatically by measuring their error rates, to select among algorithms, and to tune hyperparameters. One should avoid looking at it with one's eyes. The term Blackbox is used because this subset is only used to obtain blackbox evaluations of classifiers.

Blackbox Dev Set

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)

Which of the following best describes the intended use of the Blackbox dev set?

True or False: It is appropriate to manually examine individual examples in the Blackbox dev set.

The Blackbox dev set is named after the _____ evaluations it provides, since examples in it are never manually inspected.

Which of the following is a valid use of the Blackbox dev set?

You should avoid looking at the Blackbox dev set with your eyes.

In the running example from Machine Learning Yearning, the Blackbox dev set contains _____ examples.

Match each dev set term to its defining characteristic.

Order the steps for setting up and using a split dev set with both Eyeball and Blackbox subsets.

What size range for a Blackbox dev set does Machine Learning Yearning say is sufficient for many applications?

The term 'Blackbox' is used because this dev set subset is used only for automated evaluations without manually examining examples.

The Blackbox dev set can be used to _____ among algorithms when comparing different model approaches.

Match each permitted use of the Blackbox dev set to a description of how that use is carried out.

Order the reasoning steps that justify keeping the Blackbox dev set off-limits for manual inspection.

Question:
Based on the course material, define what the Blackbox dev set is, identify three specific tasks it can be used for, and explain the key rule regarding how developers must interact with it.

Sample answer:
The Blackbox dev set is the second subset of a split dev set that contains the remaining examples after the Eyeball dev set has been drawn. It can be used to automatically evaluate classifiers by measuring their error rates, select among different algorithms, and tune hyperparameters. The key rule for interacting with the Blackbox dev set is that developers must avoid looking at it with their eyes to ensure that evaluations remain strictly unbiased.

Key points:
- The Blackbox dev set consists of the remaining examples in a split dev set after the Eyeball dev set is established.
- It is used to automatically evaluate classifiers, select algorithms, and tune hyperparameters.
- Developers must avoid looking at the Blackbox dev set with their eyes to maintain its utility for blind, automatic evaluations.

Rubric:
The response must correctly define the Blackbox dev set as the remaining subset after the Eyeball dev set is drawn. It must list all three primary uses: evaluating classifiers automatically (measuring error rates), selecting among algorithms, and tuning hyperparameters. Finally, it must specify the rule that developers must not manually examine or look at the data.

Explain the core purpose and proper handling of a Blackbox dev set.

Case context:
An engineering team splits their dev set into an Eyeball dev set and a Blackbox dev set. After running an automated evaluation, a developer notices that the model performs poorly on the Blackbox dev set. To understand the errors, the developer opens the Blackbox dev set files and manually inspects the misclassified examples to diagnose the bugs.

Question:
Identify the methodology error in the developer's actions, explain why this action contradicts the purpose of the Blackbox dev set, and describe what the developer should have done instead to diagnose the model's issues.

Sample answer:
The methodology error is that the developer manually inspected (looked at) the examples in the Blackbox dev set. This violates its purpose because the Blackbox dev set is strictly a hands-off subset intended only for automated evaluations (measuring error rates, selecting algorithms, and tuning hyperparameters) without human inspection. By looking at the data, the developer compromises the 'blackbox' nature of the set. To diagnose the bugs, the developer should have manually inspected the misclassified examples in the Eyeball dev set instead.

Key points:
- Manually inspecting the Blackbox dev set is a methodology error.
- The Blackbox dev set must remain off-limits to manual visual inspection to preserve its integrity for automated evaluations.
- Manual error analysis and debugging must be performed using the Eyeball dev set instead.

Rubric:
The answer must identify that looking at the Blackbox dev set is the error. It must explain that this violates its purpose as a hands-off, blind evaluation subset. It must recommend using the Eyeball dev set for manual error inspection instead.

Diagnose a methodology error in a team's use of a split dev set.

Question:
According to the course text, why is this specific subset of the dev set called the 'Blackbox' dev set?

Sample answer:
The term 'Blackbox' is used because this subset of the data is only used to obtain 'Blackbox' evaluations of classifiers, meaning the models are evaluated automatically by measuring their error rates without developers manually examining the underlying examples.

Key points:
- The term 'Blackbox' originates from using the subset only to obtain 'Blackbox' evaluations of classifiers.
- It highlights that the subset is used for automated evaluations (like error rate measurements) and is not manually examined.

Rubric:
The response must state that the term is derived from the fact that the subset is used solely to obtain 'Blackbox' evaluations of classifiers, which are automated measurements that do not involve manual inspection of the data.

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