Validation involves testing the model with independent datasets to ensure its predictions hold true in real-world scenarios. This process includes cross-validation, where the data is divided into training and testing sets, and the model is trained on one set while being tested on the other. Additionally, clinical trials can be used to validate the model’s effectiveness in a controlled environment.
