`naive_Bayes()`

defines a model that uses Bayes' theorem to compute the
probability of each class, given the predictor values. 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 prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".

- smoothness
An non-negative number representing the the relative smoothness of the class boundary. Smaller examples result in model flexible boundaries and larger values generate class boundaries that are less adaptable

- Laplace
A non-negative value for the Laplace correction to smoothing low-frequency counts.

- engine
A single character string specifying what computational engine to use for fitting.

## 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
naive_Bayes(argument = !!value)
```