Ridge regression is a type of regularized linear regression that addresses the issue of multicollinearity in datasets. By adding a penalty term to the ordinary least squares (OLS) loss function, ridge regression minimizes the impact of less relevant features, leading to more stable and generalizable models. This is particularly useful in high-dimensional datasets commonly encountered in nanotechnology research.
