Reading a High-Bias Learning Curve When the Train-Dev Gap Is Small
A common sign of high bias is that, even with the largest available training set, the training error is still much higher than the target performance level. If the training error and development error are close together, the model is not showing much variance; the main problem is that it is underfitting rather than spreading its mistakes between train and dev.
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