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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.

¹ The default engine. ² Requires a parsnip extension package.

More information on how parsnip is used for modeling is at


linear_reg(mode = "regression", engine = "lm", penalty = NULL, mixture = NULL)



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


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


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


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.


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)


#> # 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 Regression Model Specification (regression)
#> Computational engine: lm