Training a predictive model involves several steps: 1. Data Collection: Gather experimental and theoretical data regarding catalytic reactions. 2. Feature Selection: Identify relevant features that influence catalytic performance. 3. Model Selection: Choose an appropriate algorithm (e.g., linear regression, neural networks). 4. Training: Use the collected data to train the model by adjusting its parameters. 5. Validation: Test the model against a separate dataset to evaluate its accuracy. 6. Refinement: Optimize the model by tuning its parameters and improving data quality.
