Poorer Out of Sample Prediction

Poorer out-of-sample prediction, the failure of models to generalize well to unseen data, is a central challenge in machine learning. Current research focuses on improving out-of-sample risk estimation through techniques like randomized leave-one-out cross-validation and developing models, such as physics-informed neural networks, that are more robust to extrapolation beyond the training data. Addressing this issue is crucial for building reliable and trustworthy machine learning systems across diverse applications, from financial forecasting to scientific simulations, as it directly impacts the practical utility and deployment of these models.