Model Generalization

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Model generalization measures how well a model can apply what it has learned to new, unseen data. A model that generalizes well captures real patterns instead of memorizing the training data. When it memorizes too much, it overfits and performs poorly on new data; when it’s too simple, it underfits and misses important relationships.

Developers use several methods to improve generalization, such as splitting data into training and test sets, applying regularization, or stopping training early when performance starts to drop. They also test models with cross-validation and track performance over time to make sure results stay consistent after deployment. In practice, good generalization means a model stays reliable even as the data or environment changes.

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