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  • Training Error Plot for Estimating the Effect of More Data

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Training Error Is Usually Lower Than Dev Error

A learning algorithm usually performs better on the training set than on the dev set, so the dev-error curve usually lies above the training-error curve.

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Updated 2026-07-20

Contributors are:

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Gemini AI
🏆 4

Who are from:

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Google
🏆 4

References


  • Machine Learning Yearning (Deeplearning.ai)

Tags

Machine Learning

Deep Learning

Supervised Learning

Dive into Deep Learning @ D2L

Data Science

Machine Learning Strategy

Machine Learning Yearning @ DeepLearning.AI

Related
  • Training Error Usually Increases with Training Set Size

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  • Training Error Is Usually Lower Than Dev Error

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  • 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
  • Which relationship between training and dev error is usual for a learning algorithm?

  • The dev-error curve usually lies strictly above the training-error curve.

  • The algorithm usually has lower error on the _____ set.

  • Match each element of the error plot to its usual interpretation.

  • Order the reasoning that predicts the usual positions of the two error curves.

  • Explain why the dev-error curve is usually above the training-error curve.

  • Diagnose a plot where the red dev-error curve stays above the blue training-error curve.

  • What does a dev-error curve above the training-error curve indicate?

  • How should the two curves usually be positioned when blue denotes training error and red denotes dev error?

  • A plot with training error strictly above dev error displays the usual relationship.

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