Ridge Regression

Ridge regression, a regularized linear regression technique, aims to improve prediction accuracy and prevent overfitting by adding a penalty term to the least-squares objective function. Current research focuses on understanding its behavior in high-dimensional settings, particularly under data correlation and distribution shifts, exploring its use in various applications like time-series forecasting, brain encoding, and federated learning, and comparing its performance against other methods, including neural networks and Bayesian approaches. This robust and computationally efficient method continues to be relevant due to its strong theoretical foundations and practical applicability across diverse fields, offering valuable insights into model generalization and regularization strategies.