A company has a machine learning model and wants insight into how the model arrives at its predictions. What is the term for understanding a model’s predictions?

Model interpretability refers to the ability to understand and explain how a machine learning model makes its predictions. This is crucial for building trust, debugging models, ensuring fairness, and complying with regulations. It involves techniques to peek inside the "black box" of complex models. Model training is the process of teaching the model using data, not understanding its predictions. Model interoperability refers to the ability of different models or systems to work together, not explaining individual predictions. Model performance measures how well the model performs its task (e.g., accuracy, precision), but doesn't explain why it made specific predictions.