Model Interpretability in Machine Learning: Studying techniques for improving the interpretability of machine learning models to gain insights into model predictions

Abstract

Machine learning models have achieved remarkable success in various fields, yet their inner workings often remain opaque, hindering their adoption in critical domains. Model interpretability aims to address this challenge by making models more transparent and understandable to humans. This paper provides a comprehensive overview of techniques for improving the interpretability of machine learning models. We discuss the importance of interpretability, review key methods and approaches, and explore their applications and implications. By enhancing interpretability, we can enhance trust, enable better decision-making, and facilitate the deployment of machine learning models in real-world settings.