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
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/.
logistic_reg( 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
A non-negative number representing the total
amount of regularization (specific engines only).
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.
show_engines("logistic_reg")#> # 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 classificationlogistic_reg()#> Logistic Regression Model Specification (classification) #> #> Computational engine: glm #>