`linear_reg()`

defines a model that can predict numeric values from
predictors using a linear function. This function can fit regression models.

There are different ways to fit this model, and the method of estimation is chosen by setting the model *engine*. The engine-specific pages for this model are listed below.

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

## Arguments

- mode
A single character string for the type of model. The only possible value for this model is "regression".

- engine
A single character string specifying what computational engine to use for fitting. Possible engines are listed below. The default for this model is

`"lm"`

.- penalty
A non-negative number representing the total amount of regularization (specific engines only).

- mixture
A number between zero and one (inclusive) denoting the proportion of L1 regularization (i.e. lasso) in the model.

`mixture = 1`

specifies a pure lasso model,`mixture = 0`

specifies a ridge regression model, and`0 < mixture < 1`

specifies an elastic net model, interpolating lasso and ridge.

Available for specific engines only.

## Details

This function only defines what *type* of model is being fit. Once an engine
is specified, the *method* to fit the model is also defined. See
`set_engine()`

for more on setting the engine, including how to set engine
arguments.

The model is not trained or fit until the `fit()`

function is used
with the data.

Each of the arguments in this function other than `mode`

and `engine`

are
captured as quosures. To pass values
programmatically, use the injection operator like so:

```
value <- 1
linear_reg(argument = !!value)
```

## See also

`fit()`

, `set_engine()`

, `update()`

, `lm engine details`

, `brulee engine details`

, `gee engine details`

, `glm engine details`

, `glmer engine details`

, `glmnet engine details`

, `gls engine details`

, `h2o engine details`

, `keras engine details`

, `lme engine details`

, `lmer engine details`

, `spark engine details`

, `stan engine details`

, `stan_glmer engine details`

## Examples

```
show_engines("linear_reg")
#> # A tibble: 7 × 2
#> engine mode
#> <chr> <chr>
#> 1 lm regression
#> 2 glm regression
#> 3 glmnet regression
#> 4 stan regression
#> 5 spark regression
#> 6 keras regression
#> 7 brulee regression
linear_reg()
#> Linear Regression Model Specification (regression)
#>
#> Computational engine: lm
#>
```