Generalization error refers to the difference between the performance of a model on a training dataset and its performance on unseen data. It's a crucial concept in machine learning as it indicates how well a model can apply what it has learned to new, previously unseen examples. A lower generalization error suggests that the model is effective and robust, while a higher generalization error indicates potential issues such as overfitting or underfitting.
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