Order the reasoning process for sizing a dev set around a meaningful accuracy change.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
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Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
Small Dev Sets Cannot Detect Tiny Accuracy Differences
Common Dev Set Sizes for Detecting 0.1% Improvements
High-Value Applications May Need Larger Dev Sets
Statistical Significance Tests on Dev Set Changes
Which dev set best supports detecting a 0.1 percentage-point accuracy improvement?
A dev set should always be made as large as possible, even beyond what meaningful-change detection requires.
A dev set of _____ examples gives a good chance of detecting a 0.1% improvement.
Match each accuracy-detection goal with its dev set implication.
Order the reasoning process for sizing a dev set around a meaningful accuracy change.
Explain how the size of a meaningful accuracy improvement should influence dev set size.
Decide whether a mature recommendation system needs a larger dev set.
Why is a 100-example dev set inadequate for comparing 90.0% and 90.1% accuracy?
When is a dev set much larger than 10,000 examples most justified?
A team satisfied with detecting meaningful changes need not enlarge its dev set far beyond that requirement.