When training-set performance is already close to the optimal error rate, there is little room for bias improvement. If the algorithm is not generalizing well to the dev set, there is ample room for variance improvement.

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Plotting both training error and dev error on the same learning curve makes it easier to extrapolate dev-error behavior. Looking at the full curve gives a more comprehensive picture of algorithm performance across different training-set sizes than measuring training and dev error only at the largest available training set size.

Interpreting Learning Curves with Training and Dev Error

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A standard learning-curve pattern for high avoidable bias occurs when, at the largest training-set size, training error remains far above the desired performance level. If the gap between training error and dev error is small, this indicates small variance despite the large avoidable bias.

Learning Curve Pattern for High Avoidable Bias

Learning Curve Pattern for High Variance

A learning curve can indicate both significant bias and significant variance when training error is much higher than the desired performance level and dev error is much higher than training error. In this case, the algorithm needs changes that reduce both bias and variance.

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