logistic_reg() defines a generalized linear model for binary outcomes. A linear combination of the predictors is used to model the log odds of an event.

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

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

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



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


A single character string specifying what computational engine to use for fitting. Possible engines are listed below. The default for this model is "glm".


A non-negative number representing the total amount of regularization (specific engines only). For keras models, this corresponds to purely L2 regularization (aka weight decay) while the other models can be either or a combination of L1 and L2 (depending on the value of mixture).


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). For LiblineaR models, mixture must be exactly 0 or 1 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.

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

See also


#> # A tibble: 6 × 2 #> engine mode #> <chr> <chr> #> 1 glm classification #> 2 glmnet classification #> 3 LiblineaR classification #> 4 spark classification #> 5 keras classification #> 6 stan classification
#> Logistic Regression Model Specification (classification) #> #> Computational engine: glm #>