The same training error can imply different bias-reduction opportunities depending on the optimal error rate. If the optimal error rate is about 0%, a training error of 15% leaves much room for improvement and suggests bias-reducing changes might be fruitful. If the optimal error rate is 14%, the same training-set performance leaves little room for improvement in the classifier's bias.

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For a problem whose optimal error rate is far from zero, that optimal error rate can be treated as unavoidable bias. In the speech-recognition example, if even the best possible speech system would still have 14% error, that 14% is the unavoidable part of the learning algorithm's bias.

Optimal Error Rate as Unavoidable Bias

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