> For the complete documentation index, see [llms.txt](https://nag-9-s.gitbook.io/machine-learning/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nag-9-s.gitbook.io/machine-learning/learning/supervised-learning/regression/comments.md).

# 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](/machine-learning/learning/supervised-learning/regression/linear-regression.md)