Risk of Merging Training Data Sources Depends on Algorithm Flexibility
Merging user-uploaded images with internet images had a real risk of worsening performance for earlier learning algorithms, such as hand-designed computer-vision features followed by a simple linear classifier. With powerful, flexible algorithms such as large neural networks, that risk has greatly diminished.
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Risk of Merging Training Data Sources Depends on Algorithm Flexibility
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Training and Dev/Test Sets from Different Distributions
Inconsistent Auxiliary Data Source
Approximating Future Dev/Test Data Before Launch
Updating Dev/Test Sets with Actual User Data After Launch
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Development Investment for Dev and Test Sets Requires Judgment
According to Machine Learning Yearning, what is the primary criterion for choosing dev and test sets?
True or False: When building a dev/test set, it is safe to assume the training distribution is the same as the test distribution.
Dev and test sets should contain examples that reflect what you ultimately want to perform well on, rather than only the _____ you happen to have for training.
Why is using a simple 30% random split of available data as your test set problematic when future data differs from training data?
According to ML Yearning, it is generally safe to assume your training data distribution is the same as your test data distribution.
Dev and test sets should be chosen to reflect data you expect to get in the _____ and want to do well on.
Match each dev/test set concept from ML Yearning to its correct description.
Order the steps for correctly choosing dev and test sets according to ML Yearning's guidance.
According to ML Yearning, what should the examples in your dev and test sets primarily reflect?
According to ML Yearning, dev and test sets must always come from the same distribution as the training data.
ML Yearning warns that the test set should not simply be _____ of the available data when future data differs from the training set.
Match each data scenario to the correct dev/test set strategy decision according to ML Yearning.
Order the reasoning steps for deciding whether a proposed dev/test set is well-chosen, per ML Yearning.
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Dev and Test Set Design for Mobile Image Applications
The Core Criterion for Dev and Test Set Selection