Model validation is a crucial step that ensures the reliability and accuracy of the predictions. This is typically achieved through:
1. Comparison with Experimental Data: Models are validated by comparing their predictions with experimental results. Any discrepancies can highlight areas that need refinement. 2. Sensitivity Analysis: This assesses how changes in model parameters affect the output, helping to identify critical variables and improve model robustness. 3. Cross-Validation: In machine learning, cross-validation techniques are used to assess the performance of predictive models.
