`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/.

logistic_reg(
mode = "classification",
engine = "glm",
penalty = NULL,
mixture = NULL
)

## 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) 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. |

## 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.

The model is not trained or fit until the `fit.model_spec()`

function is used
with the data.

## References

https://www.tidymodels.org, *Tidy Models with R*

## See also

## Examples

#> # 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_reg()

#> Logistic Regression Model Specification (classification)
#>
#> Computational engine: glm
#>