# ridge

Ridge regression

## Description

returns coefficient estimates for ridge regression
models of the predictor data `B`

= ridge(`y`

,`X`

,`k`

)`X`

and the response
`y`

. Each column of `B`

corresponds to a
particular ridge parameter `k`

. By default, the function computes
`B`

after centering and scaling the predictors to have mean 0
and standard deviation 1. Because the model does not include a constant term, do not
add a column of 1s to `X`

.

specifies the scaling for the coefficient estimates in `B`

= ridge(`y`

,`X`

,`k`

,`scaled`

)`B`

. When
`scaled`

is `1`

(default),
`ridge`

does not restore the coefficients to the original
data scale. When `scaled`

is `0`

,
`ridge`

restores the coefficients to the scale of the
original data. For more information, see Coefficient Scaling.

## Examples

## Input Arguments

## Output Arguments

## More About

## Tips

`ridge`

treats`NaN`

values in`X`

or`y`

as missing values.`ridge`

omits observations with missing values from the ridge regression fit.In general, set

`scaled`

equal to`1`

to produce plots where the coefficients are displayed on the same scale. See Ridge Regression for an example using a ridge trace plot, where the regression coefficients are displayed as a function of the ridge parameter. When making predictions, set`scaled`

equal to`0`

. For an example, see Predict Values Using Ridge Regression.

## Alternative Functionality

Ridge, lasso, and elastic net regularization are all methods for estimating the coefficients of a linear model while penalizing large coefficients. The type of penalty depends on the method (see More About for more details). To perform lasso or elastic net regularization, use

`lasso`

instead.If you have high-dimensional full or sparse predictor data, you can use

`fitrlinear`

instead of`ridge`

. When using`fitrlinear`

, specify the`'Regularization','ridge'`

name-value pair argument. Set the value of the`'Lambda'`

name-value pair argument to a vector of the ridge parameters of your choice.`fitrlinear`

returns a trained linear model`Mdl`

. You can access the coefficient estimates stored in the`Beta`

property of the model by using`Mdl.Beta`

.

## References

[1] Hoerl, A. E., and R. W. Kennard. “Ridge Regression:
Biased Estimation for Nonorthogonal Problems.”
*Technometrics*. Vol. 12, No. 1, 1970, pp. 55–67.

[2] Hoerl, A. E., and R. W. Kennard. “Ridge Regression:
Applications to Nonorthogonal Problems.” *Technometrics*.
Vol. 12, No. 1, 1970, pp. 69–82.

[3] Marquardt, D. W. “Generalized Inverses, Ridge
Regression, Biased Linear Estimation, and Nonlinear Estimation.”
*Technometrics*. Vol. 12, No. 3, 1970, pp.
591–612.

[4] Marquardt, D. W., and R. D. Snee. “Ridge Regression
in Practice.” *The American Statistician*. Vol. 29, No. 1,
1975, pp. 3–20.

## See Also

`regress`

| `stepwise`

| `fitrlinear`

| `lasso`

**Introduced before R2006a**