Generalization

In the context of data science and artificial intelligence, generalization refers to the ability of a model to perform well on new, unseen data that was not used during the training phase. A model that is able to generalize well has learned the underlying patterns and relationships in the data, rather than simply memorizing the training examples. Generalization is a key goal in machine learning, as it allows models to be applied to real-world problems and make accurate predictions on new data. However, achieving good generalization can be challenging, as models can easily overfit to the training data and fail to capture the underlying patterns. Techniques such as regularization, cross-validation, and early stopping can be used to improve generalization performance.
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