Explainable Boosting Machine

Explainable Boosting Machines (EBMs) are a class of machine learning models designed to achieve high predictive accuracy while maintaining transparency and interpretability, addressing the "black box" problem of many deep learning models. Current research focuses on applying EBMs to diverse data types, including tabular data, images, and time series, and on improving their efficiency and interpretability in high-dimensional settings through techniques like feature selection and sparsity-inducing regularization. This focus on interpretability makes EBMs valuable for applications where understanding model decisions is crucial, such as healthcare, finance, and scientific discovery, enabling greater trust and facilitating better decision-making.