Model accuracy is typically assessed through a combination of statistical metrics and validation methods:
1. Comparison with Experimental Data: Direct comparison with experimental results to evaluate the model's predictive capability. 2. Statistical Metrics: Metrics such as mean squared error (MSE), root mean square error (RMSE), and coefficient of determination (R²) are commonly used. 3. Sensitivity Analysis: Examines how variations in input parameters affect model outcomes. 4. Cross-Validation: Divides data into training and testing sets to evaluate model performance on unseen data.
