linear_reg() defines a model that can predict numeric values from predictors using a linear function.

There are different ways to fit this model. See the engine-specific pages for more details:

• lm (default)

• glmnet

• stan

• spark

• keras

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

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

## Arguments

mode 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) that is the proportion of L1 regularization (i.e. lasso) in the model. When mixture = 1, it is a pure lasso model while mixture = 0 indicates that ridge regression is being used (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.

The model is not trained or fit until the fit.model_spec() function is used with the data.

## References

fit.model_spec(), set_engine(), update(), lm engine details, glmnet engine details, stan engine details, spark engine details, keras engine details

## Examples

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