linear_reg() defines a model that can predict numeric values from
predictors using a linear function. This function can fit regression models.
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)
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
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.
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
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
captured as quosures. To pass values
programmatically, use the injection operator like so:
value <- 1 linear_reg(argument = !!value)
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