AI Glossary Term
What does “Generalization” mean?
How well a model performs on new, unseen data outside the training set.
How Generalization works
Example
A student who memorizes practice-test answers may fail a new exam, while one who understands the topic can answer new questions. A model that generalizes behaves like the second student on data it has never seen.
Why it matters
The goal of machine learning is good performance on new data, not on the training set. Generalization is what separates a useful model from a memorizer.
Common questions
How do I measure generalization?
By testing on held-out data the model has not trained on and comparing with its training performance.
What hurts generalization?
Overfitting, small or unrepresentative training data, and differences between training and real-world conditions.
How can I improve it?
More diverse data, regularization, data augmentation, and simpler models where appropriate.