Which statement about ridge regression is false?

Ridge regression is a technique used for linear regression that introduces a penalty to the size of the coefficients to help mitigate issues such as multicollinearity and overfitting. It achieves this by adding a regularization term to the loss function, which effectively shrinks the coefficient estimates.

The statement regarding larger estimates without any penalty is inaccurate because the primary function of ridge regression is to apply a penalty (specifically, the L2 penalty) based on the size of the coefficients. This penalty discourages large coefficients, ultimately leading to more manageable and often more interpretable models. Instead of allowing coefficients to grow indefinitely, ridge regression constrains them, leading to smaller estimates for coefficients that minimize overfitting while maintaining predictive power.

Consequently, the other statements align with the properties of ridge regression—coefficients are indeed shrunk, it can help reduce bias by preventing overfitting, and it is effective in handling multicollinearity by adjusting coefficients to stabilize the estimates. The penalty applied by ridge regression serves as a crucial aspect of its functionality, making the false statement about larger estimates without any penalty distinctly evident.