Key things to know about Ridge Regression:

• It is a regularization technique used to address the problem of multicollinearity in linear regression models.

• Multicollinearity occurs when independent variables are highly correlated, which increases the variance of the coefficient estimates.

• Ridge adds a degree of “bias” to the coefficient estimates by imposing a penalty on the size of coefficients.

• It works by adding the L2 norm (square) of the coefficients to the loss function that is being minimized during regression.

• This shrinks the large coefficients and distributes the weight more evenly among correlated variables, improving generalization.

• The shrinkage is controlled by a hyperparameter alpha. Higher alpha means more shrinkage of coefficients towards zero.

• It helps avoid overfitting and gives more stable and reliable estimates compared to ordinary least squares regression.

• Coefficients never become exactly zero but are shrunken, so all variables are retained in the model unlike LASSO.

• Commonly used when there are many correlated predictors to get a stable set of predictors with predictive power.

So in summary, Ridge applies L2 regularization to linear models by imposing a penalty on large coefficients to address multicollinearity.