Learn Before
Small Training Sets Can Make Learning Curves Noisy
When the training set is small, learning-curve points can fluctuate because randomly chosen small subsets may be unusually good or bad, such as containing many ambiguous or mislabeled examples. Skewed class distributions or many-class problems increase the chance of selecting an unrepresentative small subset.
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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)
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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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Learn After
Averaging Learning Curves over Multiple Random Subsets
Balanced Subsets for Noisy Learning Curves in Skewed or Many-Class Data
Why can a learning-curve point fluctuate when it is based on a very small random training subset?
A small random subset can produce a learning-curve value that is higher or lower than expected.
A small subset with many ambiguous or mislabeled examples is unusually _____.
Match each small-subset condition to its learning-curve implication.
Order the reasoning used to diagnose a noisy point at a small training-set size.
Explain why small training subsets can make learning curves noisy.
Diagnose a noisy learning-curve point from a ten-example subset.
How do unusually good and bad small subsets affect learning-curve points?
Which dataset condition most increases the risk that a tiny random subset will be unrepresentative?
With many classes, a small random subset is less likely to be unrepresentative.