Prediction Error

Prediction error, the discrepancy between a model's predictions and actual outcomes, is a central concern across machine learning and statistics. Current research focuses on understanding prediction error in high-dimensional settings, particularly concerning overparameterized models and the impact of data distribution shifts (e.g., covariate shift), employing techniques like cross-validation, Bayesian methods, and normalizing flows to improve error estimation and model calibration. These advancements are crucial for building reliable and robust machine learning systems across diverse applications, from autonomous vehicles to medical diagnosis, where accurate prediction and uncertainty quantification are paramount.