`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/.

## Arguments

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

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

`"glm"`

.- penalty
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`

).- mixture
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 specific engines only. For

`LiblineaR`

models,`mixture`

must be exactly 1 or 0 only.

## Details

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.

Each of the arguments in this function other than `mode`

and `engine`

are
captured as quosures. To pass values
programmatically, use the injection operator like so:

```
value <- 1
logistic_reg(argument = !!value)
```

This model fits a classification model for binary outcomes; for
multiclass outcomes, see `multinom_reg()`

.

## Examples

```
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
#>
```