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A Big Gap Between Training and Development Error Suggests Overfitting
In a document-classification task, suppose a model gets 3% error on the training set but 14% error on the development set. That pattern indicates high variance: the model fits the training data well but does not generalize well to new examples. This kind of generalization failure is also called overfitting.
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