Generalization

Generalization in machine learning is the model’s ability to perform well on new, unseen data, not just the data it was trained on. It measures how well the model captures the underlying patterns rather than memorizing the training examples. Poor generalization usually means overfitting—when a model learns noise or details specific to the training set instead of general rules.