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Repeatedly evaluating ideas on the dev set can cause an algorithm to gradually overfit to the dev set. If dev set performance is much better than test set performance when development is finished, that is a sign of dev-set overfitting. In that case, get a fresh dev set.

Dev Set Overfitting from Repeated Evaluation

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)

A team may evaluate the system on the test set regularly to track progress, such as weekly or monthly. The test set should not be used to make algorithm decisions, including whether to roll back to a previous system. Using the test set for such decisions can cause overfitting to the test set, so the test set can no longer be counted on to give a completely unbiased estimate of system performance.

Avoiding Test Set Decisions During Regular Progress Tracking

What causes an algorithm to gradually overfit to the dev set during the development process?

If dev set performance is much better than test set performance after development, this is a sign of dev-set overfitting.

When dev set performance is much better than test set performance, Machine Learning Yearning recommends you get a _____ dev set.

Match each concept related to dev-set overfitting to its correct description.

Arrange the steps of the full dev-set overfitting lifecycle in the correct order.

After development, your dev set performance is far better than your test set performance. What does Machine Learning Yearning recommend?

According to Machine Learning Yearning, you should regularly evaluate your algorithm on the test set throughout development to track progress.

The process of repeatedly evaluating ideas on the dev set causes your algorithm to gradually _____ to the dev set.

Match each verbatim phrase from Machine Learning Yearning to what it refers to or signifies.

Arrange the reasoning steps for diagnosing and responding to a dev-test performance gap in the correct order.

Question:
Explain how the process of repeatedly evaluating ideas during development can lead to dev set overfitting. Furthermore, describe how you would diagnose this issue at the end of development and what specific action you should take to resolve it.

Sample answer:
Repeatedly evaluating ideas on the dev set causes the algorithm to gradually overfit to the specific characteristics of that dev set. This is diagnosed at the end of development by comparing performance on the dev set versus the test set; if dev set performance is significantly better, overfitting has occurred. To resolve this, you should discard the overfit dev set and get a fresh dev set.

Key points:
- Repeated evaluation on the dev set causes gradual overfitting.
- Diagnosis requires comparing dev set performance against test set performance after development.
- A large gap where dev set performance is better indicates overfitting.
- The recommended solution is to get a fresh dev set.

Rubric:
The student should clearly identify the cause (repeated evaluation), the diagnosis method (comparing dev and test set performance), and the solution (getting a fresh dev set).

Analyzing the Cause and Solution for Dev Set Overfitting

Case context:
Your team has just finished a three-month development cycle for a new image classification algorithm. Throughout the process, the team continuously checked performance against the dev set, tweaking the architecture to maximize accuracy. When development concluded, the algorithm achieved 98% accuracy on the dev set but only 82% on the test set.

Question:
Based on the provided scenario, what specific problem should you diagnose regarding your dataset evaluation, and what is the recommended next step to address this issue?

Sample answer:
The team should diagnose that the algorithm has gradually overfit to the dev set due to repeated evaluation during the three-month cycle. This is evident from the massive performance gap (98% vs 82%) between the dev and test sets. The recommended next step is to get a fresh dev set to replace the one that the algorithm has overfit to.

Key points:
- Diagnose dev-set overfitting.
- Identify that the large gap between dev (98%) and test (82%) performance indicates the overfitting.
- Recommend getting a fresh dev set.

Rubric:
Full credit is given for diagnosing dev-set overfitting due to repeated evaluations and prescribing the acquisition of a new dev set.

Diagnosing a Performance Gap After Development

Question:
After concluding the development of your algorithm, what specific performance comparison serves as a sign that your algorithm has overfit to the dev set?

Sample answer:
Dev-set overfitting is indicated when you evaluate your completed system and find that the dev set performance is much better than the test set performance.

Key points:
- Compare dev set performance to test set performance.
- Dev set performance being much better is the sign of overfitting.

Rubric:
The answer must mention comparing dev set and test set performance, noting that the former being significantly higher is the key indicator.

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