Generalization error refers to the difference between the performance of a model on training data and its performance on unseen data. It provides insight into how well a model can apply what it has learned to new, unseen situations. A low generalization error indicates that a model has effectively captured the underlying patterns in the data, while a high generalization error may suggest overfitting or underfitting issues.
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