What are the Challenges in Using Statistical Models in Catalysis?
While statistical models offer numerous advantages, they also come with certain challenges: 1. Data Quality: The accuracy of the model depends on the quality of the input data. 2. Model Selection: Choosing the appropriate model for a given dataset can be difficult. 3. Overfitting: Models that are too complex may fit the training data well but perform poorly on new data. 4. Interpretability: Complex models, especially in machine learning, can be difficult to interpret.
