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. This function can fit classification 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.
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
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
A non-negative number representing the total amount of regularization (specific engines only). For
kerasmodels, 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
A number between zero and one (inclusive) giving the proportion of L1 regularization (i.e. lasso) in the model.
mixture = 1specifies a pure lasso model,
mixture = 0specifies a ridge regression model, and
0 < mixture < 1specifies an elastic net model, interpolating lasso and ridge.
Available for specific engines only. For
mixturemust be exactly 1 or 0 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.
value <- 1 logistic_reg(argument = !!value)
This model fits a classification model for binary outcomes; for
multiclass outcomes, see
show_engines("logistic_reg") #> # A tibble: 7 × 2 #> engine mode #> <chr> <chr> #> 1 glm classification #> 2 glmnet classification #> 3 LiblineaR classification #> 4 spark classification #> 5 keras classification #> 6 stan classification #> 7 brulee classification logistic_reg() #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm #>