Theoretical Statistics
Generalization error refers to the difference between the expected performance of a statistical model on unseen data and its performance on the training data. This concept is crucial as it highlights how well a model can apply what it has learned to new, unseen situations rather than just memorizing the training data. It connects closely with loss functions, which are used to quantify how well the model's predictions align with actual outcomes, influencing the overall model's ability to generalize beyond its training set.
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