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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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.
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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.