Interpretability

Interpretability refers to how easily a human can understand the reasoning behind a machine learning model's predictions. An interpretable model allows us to grasp why it made a particular decision, fostering trust and enabling better debugging or improvement. This is especially important in critical applications like healthcare or finance.