In parsnip: A Common API to Modeling and Analysis Functions

#| child: aaa.Rmd
#| include: false

r descr_models("mlp", "keras")

Tuning Parameters

#| label: keras-param-info
#| echo: false
defaults <- 
  tibble::tibble(parsnip = c("hidden_units", "penalty", "dropout", "epochs", "activation"),
                 default = c("5L", "0.0", "0.0", "20L", "'softmax'"))

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

This model has r nrow(param) tuning parameters:

#| label: keras-param-list
#| echo: false
#| results: asis
param$item

Translation from parsnip to the original package (regression)

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

Translation from parsnip to the original package (classification)

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

Preprocessing requirements

#| child: template-makes-dummies.Rmd

#| child: template-same-scale.Rmd

Case weights

#| child: template-no-case-weights.Rmd

Saving fitted model objects

#| child: template-bundle.Rmd

Examples

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

References

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

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