Model interpretation refers to the process of understanding and explaining how a machine learning model makes predictions, which is crucial for building trust, ensuring fairness, and improving model performance. It involves techniques and tools that provide insights into the model's decision-making process, enabling stakeholders to assess its reliability and biases.
