Explain why different training and evaluation distributions can be appropriate.
Question: In a concise analytical response, explain why a cat classifier might train on internet images plus mobile images while using only mobile images for dev and test sets.
Sample answer: The additional internet images can help train a data-hungry algorithm by providing a much larger training set. However, user-uploaded mobile images closely reflect the actual distribution on which the app must perform well. Dev and test sets should therefore contain mobile images so that evaluation measures the desired future performance. This creates different training and dev/test distributions by design.
Key points:
- Internet images provide additional training data.
- Mobile images represent the actual distribution of interest.
- Dev and test sets should reflect expected future data.
- Training and dev/test distributions can intentionally differ.
Rubric: A strong response identifies the value of auxiliary internet data, identifies mobile images as the target distribution, connects dev/test selection to future performance, and explains why the resulting distributions differ.
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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)
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
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Data Mismatch Between Training and Dev Set Distributions
Limited Practical Scope of Domain Adaptation for Different Data Distributions
Domain Adaptation for Different Data Distributions
Website Images and Mobile Phone Pictures as a Distribution Mismatch Example
Random 70/30 Train/Test Split Can Fail Under Distribution Shift
Which data should define the dev and test sets for the cat-picture app?
Dev and test sets should represent the future data distribution of interest.
Complete the principle: Dev and test sets should reflect _____ data.
Match each cat-app data group with its appropriate role or property.
Order the decisions for building datasets when auxiliary and target data differ.
Explain why different training and evaluation distributions can be appropriate.
Diagnose the evaluation-set mistake in a mobile cat-classification app.
Why did success on website images fail to ensure success on mobile uploads?
Which dataset design best uses both target and auxiliary cat images?
Using extra internet images for training requires internet images in dev and test sets.