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)