Experimental data is indispensable for achieving predictive accuracy. It serves several purposes:
Model Validation: Experimental results are used to validate and refine computational models and ML algorithms. Benchmarking: Provides a benchmark against which predictive models can be evaluated. Data Augmentation: Enhances the training datasets used in machine learning, improving model accuracy.
