Training Error Usually Increases with Training Set Size
As training-set size grows, dev and test error should decrease, but training-set error usually increases. With very small training sets, an algorithm can often memorize the examples and get near-zero training error; with larger and more ambiguous or mislabeled training sets, perfectly fitting every example becomes harder.
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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)
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Supervised Learning
Dive into Deep Learning @ D2L
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Machine Learning Yearning @ DeepLearning.AI
Related
Training Error Usually Increases with Training Set Size
Training Error Is Usually Lower Than Dev Error
Why should training error be plotted when evaluating whether more data may help?
A dev-error curve alone may be difficult to extrapolate to larger training sets.
The additional plot used to estimate the effect of more data is _____.
Match each learning-curve element to its role in estimating the effect of more data.
Order the reasoning process for assessing the possible impact of adding data.
Explain why training error strengthens an analysis of whether more data may help.
A team cannot confidently project its dev-error curve. What analysis should it add?
What limitation motivates adding a training-error plot?
Which analysis best follows the source when considering a larger dataset?
Training error is intended to supplement, rather than replace, the dev-error curve.
Learn After
How does training error usually change as the training set grows?
A tiny training set can make training performance look deceptively strong.
As training-set size grows, training error usually _____.
Match each dataset condition to its expected error behavior.
Order the reasoning that explains rising training error.
Explain why more training data can raise training error while lowering dev error.
Diagnose the error trends after a cat classifier receives more training examples.
Why can two training examples produce 0% training error?
Which observation best fits the expected learning-curve pattern?
A rise in training error with more data necessarily contradicts the expected pattern.