What should a team do after the development set stops guiding decisions?
Question: A machine learning team notices that its development set and evaluation metric are no longer useful for choosing models because the team has started fitting the development set too closely. What action should they take?
Sample answer: They should refresh the development set by collecting additional development examples.
Key points:
- The problem is that the team has adapted too much to the current development set.
- The fix is to expand or replace it with more development data.
Rubric: The answer must state that the team should obtain more development set data to address the overfitting problem.
0
1
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
When Evaluation Data Does Not Match Deployment Data
When Repeated Validation Checks Distort Model Selection
When the Metric Rewards the Wrong Goal
When should your validation setup be revised?
True or False: If your initial validation split or evaluation metric turns out to be poorly chosen, you cannot revise it without abandoning the project.
If your evaluation metric no longer reflects your main objective, what should you change?
What is the clearest sign that your dev/test set or evaluation metric may need revision?
If a validation set or metric turns out to be poorly matched to the real goal, the team should rebuild the whole project before making any changes.
What to revise when the evaluation no longer matches the goal
Match each reason a validation metric can mislead the team to the recommended remedy.
What should a team do when its evaluation setup stops matching its goal?
When Validation Data Does Not Match Deployment Data
After revising your dev/test sets or evaluation metric, updating the project documentation is enough; the team does not need to be told about the new direction.
What should be expanded after repeated tuning to the validation set?
Match each situation to the underlying problem category it illustrates.
Order the reasoning steps for deciding whether to replace an evaluation metric that no longer matches the product goal.
When validation results stop matching the best product choice
When Evaluation Scores and Product Needs Disagree
What should a team do after the development set stops guiding decisions?