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poisson_reg() defines a generalized linear model for count data that follow a Poisson distribution. 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


  mode = "regression",
  penalty = NULL,
  mixture = NULL,
  engine = "glm"



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


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


A number between zero and one (inclusive) giving 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 glmnet and spark only.


A single character string specifying what computational engine to use for fitting.


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.