Underfitting a supervised statistical model
A model that is too simple to capture the underlying pattern in the training data is said to underfit: it performs poorly even on the training data itself, and is therefore unlikely to generalize well to new, unseen examples. Underfitting is typically associated with high bias and is the opposite failure mode of overfitting.
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Overfitting a supervised statistical model
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