Model interpretability refers to the degree to which a human can understand the reasons behind a model’s decisions or predictions. It is crucial in machine learning, as it allows users to comprehend how models arrive at their outputs, which can enhance trust and facilitate better decision-making. Interpretability can be defined in two ways: the extent to which a human can understand the cause of a decision and the ability to consistently predict the model’s results ³.

There are various models that are inherently interpretable, such as linear regression, logistic regression, and decision trees. These models allow users to easily grasp the relationships between input features and the target variable ². However, there is often a trade-off between model accuracy and interpretability; highly accurate models may be more complex and less interpretable ⁴.

In practice, tools like LIME can help interpret more complex models, but it is essential to be cautious about trusting models that claim high accuracy without understanding their inner workings ¹.