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 enginespecific pages for more details:
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)
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 
penalty  A nonnegative number representing the total amount of regularization (specific engines only). 
mixture  A number between zero and one (inclusive) that is the
proportion of L1 regularization (i.e. lasso) in the model. When

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.
https://www.tidymodels.org, Tidy Models with R
fit.model_spec()
, set_engine()
, update()
, lm engine details
, glmnet engine details
, stan engine details
, spark engine details
, keras engine details
#> # A tibble: 5 × 2 #> engine mode #> <chr> <chr> #> 1 lm regression #> 2 glmnet regression #> 3 stan regression #> 4 spark regression #> 5 keras regressionlinear_reg()#> Linear Regression Model Specification (regression) #> #> Computational engine: lm #>