Shadow modeling refers to the practice of creating a simplified or surrogate model that approximates the behavior of a more complex system, often used in machine learning to improve efficiency or interpretability. This approach is particularly useful for understanding and predicting the output of black-box models, enabling insights without requiring full access to the original model's data or architecture.