Generalized Additive Model

Generalized Additive Models (GAMs) are a class of machine learning models designed for both high predictive accuracy and interpretability, particularly valuable when analyzing tabular data. Current research focuses on enhancing GAM performance through novel architectures like neural additive models and incorporating techniques such as in-context learning and sparse regularization to improve efficiency and feature selection. This emphasis on interpretability and improved performance makes GAMs increasingly important for applications requiring transparent decision-making, such as healthcare, finance, and energy forecasting, while also advancing the broader field of explainable AI.