Empirical Model Selection

Understanding the importance of trial and error is crucial when selecting machine learning models. By leveraging benchmarks and creating prototypes with subsets of data, you can guide your decisions effectively. Additionally, using real platform data to seed simulations can enhance the relevance of your test cases, ensuring that your synthetic data covers a wide range of user scenarios.