`bag_mlp()`

defines an ensemble of single layer, feed-forward neural networks.
This function can fit classification and regression 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.

`nnet¹²`

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

## Usage

```
bag_mlp(
mode = "unknown",
hidden_units = NULL,
penalty = NULL,
epochs = NULL,
engine = "nnet"
)
```

## Arguments

- mode
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".

- hidden_units
An integer for the number of units in the hidden model.

- penalty
A non-negative numeric value for the amount of weight decay.

- epochs
An integer for the number of training iterations.

- 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
bag_mlp(argument = !!value)
```