Interpretable models are machine learning models that are designed to provide clear explanations or insights into how they arrive at their predictions or decisions. These models are often used in applications where understanding the reasoning behind a model's output is important, such as in healthcare, finance, and law. Interpretable models are intended to be more transparent and understandable to non-experts compared to more complex black-box models, such as deep neural networks. Techniques used to create interpretable models include decision trees, linear regression, rule-based models, and feature importance analysis. The goal of interpretable models is to increase trust, accountability, and usability in machine learning systems.