In parsnip: A Common API to Modeling and Analysis Functions

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r descr_models("mlp", "nnet")

Tuning Parameters

#| label: nnet-param-info
#| echo: false
defaults <- 
  tibble::tibble(parsnip = c("hidden_units", "penalty", "epochs"),
                 default = c("none", "0.0", "100L"))

param <-
  mlp() |> 
  set_engine("nnet") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

#| label: nnet-param-list
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param$item

Note that, in [nnet::nnet()], the maximum number of parameters is an argument with a fairly low value of maxit = 1000. For some models, you may need to pass this value in via [set_engine()] so that the model does not fail.

Translation from parsnip to the original package (regression)

#| label: nnet-reg
mlp(
  hidden_units = integer(1),
  penalty = double(1),
  epochs = integer(1)
) |>  
  set_engine("nnet") |> 
  set_mode("regression") |> 
  translate()

Note that parsnip automatically sets linear activation in the last layer.

Translation from parsnip to the original package (classification)

#| label: nnet-cls
mlp(
  hidden_units = integer(1),
  penalty = double(1),
  epochs = integer(1)
) |> 
  set_engine("nnet") |> 
  set_mode("classification") |> 
  translate()

Preprocessing requirements

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Case weights

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Saving fitted model objects

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Examples

The "Fitting and Predicting with parsnip" article contains examples for mlp() with the "nnet" engine.

References

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

parsnip documentation built on June 8, 2025, 12:10 p.m.