# Comments

In linear regression, only the **residual sum of squares** (**RSS**) is minimized, whereas in ridge and lasso regression, a penalty is applied (also known as **shrinkage penalty**) on coefficient values to regularize the coefficients with the tuning parameter *λ*.

When *λ=0*, the penalty has no impact, ridge/lasso produces the same result as linear regression, whereas *λ -> ∞* will bring coefficients to zero:

[Linear regression](https://nag-9-s.gitbook.io/machine-learning/learning/supervised-learning/regression/linear-regression)