Choose evaluation data to match the real-world target
A model is often trained on one kind of data but ultimately needs to work well on a different kind of data. For example, a wildlife classifier might be trained mostly on clear camera-trap photos, but the real deployment goal is to recognize animals in blurry field-camera images taken at night. In that situation, the development and test sets should be built from the same kind of images the model will face after deployment, because those sets should reflect the future data distribution that matters most.
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Choose evaluation data to match the real-world target
Mismatched Auxiliary Data Source
Building Dev and Test Sets Before Real Users Exist
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Judging How Much to Invest in Dev and Test Sets
What should determine dev and test set selection?
True or False: You can assume the training set and test set always come from the same distribution.
Development and test sets should reflect the conditions you expect after deployment, not only the _____ available in your training pool.
Why can a simple random test split be a poor choice when the data you expect in the future is different from the data you have now?
You can usually assume the data used for training and the data used for testing come from the same distribution.
Design Dev and Test Sets for the Future
Match each concept about development and test sets to its description.
Order the steps for choosing development and test sets when future data differs from training data.
What should dev and test examples be designed to resemble?
A validation and test set must exactly match the training distribution in every project.
How should a test set be chosen when deployment data will differ?
Match each data scenario to the best dev/test set choice.
Order the reasoning steps for deciding whether a dev/test split is appropriate.
Why a Random 30 Percent Split Can Be Misleading When Future Data Will Differ
Dev and Test Splits for a Field-Photo Classifier
How to Choose Dev and Test Data When Future Data Will Differ
Learn After
Do Not Randomly Mix Sources When Building Validation and Test Sets
Use a Small Amount of Target-Distribution Data in Training with Auxiliary Data
When to Reduce the Influence of Extra Training Data
Training-Development Set
Comparing Errors Across Two Data Sources
Training–Dev Distribution Gap
Domain adaptation solves only certain distribution-shift problems
Choosing Development and Test Data for a Specific Application
Factory Photos and Inspection Tablet Images as a Distribution Mismatch Example
Random Train/Test Splits Can Be Misleading When Deployment Data Differs
Which data should define the dev and test sets for the plant-disease app?
Development and test examples should be drawn to match the kind of data the deployed system is expected to see.
Development and test sets should match the kind of data you expect later.
Match each data group for the plant-disease app with its role.
Order the dataset choices when training data and evaluation data come from different sources.
Why training data and evaluation data may come from different sources
Find the evaluation-set mismatch in a voice-command detector.
Why did strong desktop-photo results not predict mobile-upload success?
Choosing Training and Evaluation Data from Two Image Sources
If training uses extra web-sourced photos, the dev and test sets must also contain web-sourced photos.