Ridge
Ridge regression is a variant of linear regression that introduces an L2 penalty term to the loss function to heavily shrink excessively large coefficients.
Think of It Like This
Like a strict manager forcing every employee to share the workload evenly, rather than letting one superstar employee do absolutely everything alone.
By penalizing the squared magnitude of the model's weights, Ridge effectively combats multicollinearity and prevents overfitting. Unlike Lasso (L1 regularization) which can shrink coefficients to exactly zero, Ridge smoothly shrinks them towards zero but keeps all features active. It provides a much more stable model when features are highly correlated.