In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("mlp", "keras")

Tuning Parameters

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:

param$item

Translation from parsnip to the original package (regression)

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)

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

Case weights

Saving fitted model objects

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 24, 2024, 5:14 p.m.