Generalization is a model’s ability to apply what it learned from training data to new, unseen examples. Good generalization indicates that the model has learned useful patterns rather than merely memorizing the training set.
The concept that humans, other animals, and artificial neural networks use past learning in present situations of learning if the conditions in the situations are regarded as similar.
A model's ability to make correct predictions on new, previously unseen data. A model that can generalize is the opposite of a model that is overfitting. You train a model on the examples in the training set. Consequently, the model learns the peculiarities of the data in the training set. Generalization essentially asks whether your model can make good predictions on examples that are not in the training set. To encourage generalization, regularization helps a model train less exactly to the peculiarities of the data in the training set. See Generalization in Machine Learning Crash Course for more information.
A model's ability to make correct inferences on new, previously unseen data. A model that can generalize is the opposite of a model that is overfitting.