linear_reg: Linear regression

View source: R/linear_reg.R

linear_regR Documentation

Linear regression


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


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.

References, Tidy Modeling with R, searchable table of parsnip models

See Also





parsnip documentation built on June 16, 2022, 5:10 p.m.