bag_mlp: Ensembles of neural networks

View source: R/bag_mlp.R

bag_mlpR Documentation

Ensembles of neural networks


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


More information on how parsnip is used for modeling is at


  mode = "unknown",
  hidden_units = NULL,
  penalty = NULL,
  epochs = NULL,
  engine = "nnet"



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


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


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


An integer for the number of training iterations.


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


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)

References, Tidy Modeling with R, searchable table of parsnip models

See Also


parsnip documentation built on Aug. 18, 2023, 1:07 a.m.