Use Matching Dev and Test Distributions for Application Work
When the goal is to improve one specific machine learning application, the development set and test set should usually come from the same data distribution. That gives the team a clearer signal about real product performance and makes iteration more efficient.
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Use Matching Dev and Test Distributions for Application Work
For an application project, what data-splitting rule is most helpful?
Domain adaptation methods are the default solution for most machine learning projects.
Domain adaptation means training on one _____ and expecting useful performance on another.
Match each machine-learning concept to its description.
Order the steps for choosing dev/test data when the project data differs from deployment data.
When are domain adaptation methods usually a good fit?
Selecting development and test sets from the same data distribution can improve team efficiency.
Dev and test sets should come from the _____ distribution.
Match each project goal to the strategy associated with it.
Order the main ideas behind matching dev and test data to the deployment setting.
Domain Adaptation Research Versus Application-Focused Data Splits
Choose evaluation strategy for a crop disease app.
Why choose dev and test sets from the same distribution instead of building a special adaptation method?
Learn After
When the goal is to improve one deployed customer-support classifier, what should the development and test sets represent?
True or False: Training a model on one data distribution and getting it to work well on a different distribution is an important research challenge.
For reliable progress on one specific ML application, the development and test sets should come from the _____ distribution as the data the system will face in practice.
For an application-focused machine learning project, what dev/test setup is usually recommended?
True or False: For a focused machine learning product, using dev and test sets drawn from the same distribution usually helps the team move faster.
For an application to be judged fairly, the development and test sets should come from the _____ distribution.
Match each term to its best description in a discussion of how to choose dev and test sets.
Put the steps in order for choosing dev and test distributions in an application-driven ML project.
Which data-distribution issue is treated as a hard research challenge?
A development and test set drawn from the same distribution is equally emphasized for both application-focused progress and research-focused progress.
Using dev and test sets from the same distribution makes your _____ coordination more efficient.
Match each recommendation or outcome to the project goal it best supports.
Order the steps for choosing development and test distributions when planning a machine learning project.
Why matching the dev and test distributions can improve development speed
Aligning Development and Test Data for Faster Progress
How Should Dev and Test Sets Be Chosen for Product Work Versus General Research?