Interpretability in Machine Learning Models

Abstract

Interpretability in machine learning models has become increasingly important as these models are deployed in critical applications such as healthcare, finance, and autonomous vehicles. Understanding how these models make predictions is crucial for gaining trust from users and stakeholders, ensuring fairness, and identifying potential biases. This paper provides a comprehensive review of interpretability techniques for machine learning models, ranging from simple, model-agnostic methods to more complex, model-specific approaches. We discuss the importance of interpretability, explore various techniques, and evaluate their effectiveness in improving the understanding of model predictions. Additionally, we highlight challenges and future directions in this field to guide further research and development.