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

¹ The default engine. ² Requires a parsnip extension package for classification and regression.

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.

## References

fit(), set_engine(), update(), nnet engine details