Underfitting
When a model is too simple to capture the patterns in the data.
Underfitting occurs when a model lacks the capacity or training to learn the underlying patterns in data, resulting in high error on both training and test sets. Unlike overfitting, where the model memorizes noise, an underfit model fails even on the data it was trained on — it is too rigid to generalize because it never learned enough to begin with. On the AIF-C01 exam, the key distinction is that underfitting signals a model complexity problem or insufficient training, not a data-leakage problem. In Amazon SageMaker, common remedies include choosing a more expressive algorithm, training for more epochs, or engineering richer features — rather than applying regularization, which addresses overfitting.
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