Ridgeless Regression

Ridgeless regression, a type of regression analysis that omits the regularization term typically used to prevent overfitting, has become a focus of intense research due to its surprising ability to generalize well despite interpolating noisy data—a phenomenon known as benign overfitting. Current research investigates the conditions under which this occurs, exploring various kernel functions (e.g., Gaussian, RBF) and examining the impact of factors like dimensionality, bandwidth selection, and the underlying data structure (e.g., time series). Understanding the behavior of ridgeless regression offers valuable insights into the generalization capabilities of machine learning models and may lead to improved algorithms with enhanced efficiency and robustness.