Validation of predictive models is a critical step to ensure their reliability. This can be done through:
Cross-validation: Splitting the dataset into training and testing subsets to evaluate model performance. Experimental Validation: Comparing model predictions with experimental results to assess accuracy. Sensitivity Analysis: Examining how changes in model inputs affect outputs to understand robustness.
