Validation is vital for confirming the generalizability of a risk prediction model.
- Internal Validation: This involves testing the model on the same dataset used for its development, often using techniques like cross-validation or bootstrapping.
- External Validation: This is more rigorous and involves testing the model on a different dataset from the one used for development. It assesses the model's performance in a new, independent population.
