What is the key characteristic of Ridge regularization?

Ridge regularization is characterized by its application of L2 penalty to the coefficients in a regression model. This form of regularization works by adding a penalty equal to the square of the magnitude of coefficients to the loss function. The primary goal of Ridge regularization is to prevent overfitting by reducing the complexity of the model. By applying the L2 penalty, Ridge encourages the model to keep all coefficients small, but unlike Lasso (which employs L1 penalty), it does not set any coefficients exactly to zero. Therefore, Ridge regression retains all features in the model while managing their influence through shrinkage, effectively leading to more stable predictions.

In contrast, simply shrinking coefficients to zero is not a characteristic of Ridge regularization since that behavior is typical of Lasso regularization. While model tuning can be an aspect of various regularization methods, Ridge regularization does typically require tuning the regularization strength parameter, which adjusts the degree of penalty applied to the coefficients. Additionally, the L1 penalty refers specifically to Lasso regularization, not Ridge.