Model interpretability refers to the degree to which a human can understand the reasons behind a model’s decisions or predictions. It is essential for ensuring trust and transparency in machine learning applications. A model is considered interpretable if its decisions are easier for a human to comprehend compared to other models. This understanding can help users predict the model’s results consistently ³.

There are various approaches to achieving interpretability. One effective method is to use inherently interpretable models, such as linear regression, logistic regression, and decision trees. These models allow for straightforward interpretation of their outputs and the relationships between features and target variables ².

However, there is often a trade-off between accuracy and interpretability. Highly accurate models, such as deep learning algorithms, may be less interpretable, making it challenging to understand their decision-making processes ⁴. Therefore, selecting the right model involves balancing the need for accuracy with the necessity of interpretability, depending on the specific application and context ⁴.