Neural Additive Model

Neural Additive Models (NAMs) are a class of interpretable machine learning models designed to combine the predictive power of deep neural networks with the transparency of additive models. Current research focuses on enhancing NAM architectures, such as incorporating Bayesian principles, prototype-based feature activation, and hierarchical structures, to improve accuracy and interpretability, particularly for complex datasets and tasks like clustering and time series forecasting. This approach addresses the critical need for explainable AI in high-stakes domains like healthcare and finance, where understanding model decisions is paramount for trust and responsible deployment. The resulting models offer improved accuracy while providing insights into feature contributions, facilitating better decision-making and knowledge discovery.