seniorSupervised Learning
What is model generalization and how is it measured?
Updated May 17, 2026
Short answer
Generalization is a model’s ability to perform well on unseen data.
Deep explanation
Generalization measures how well a trained model applies learned patterns to new, unseen data. It is evaluated using test sets, cross-validation, and metrics like accuracy, F1-score, or RMSE. A model that memorizes training data (overfitting) has poor generalization. Techniques like regularization, dropout, and data augmentation improve it.
Real-world example
A speech recognition model working well on new speakers and accents.
Common mistakes
- Equating high training accuracy with good generalization.
Follow-up questions
- What is generalization gap?
- How do you improve generalization?