In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("mlp", "brulee_two_layer")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("hidden_units", "penalty", "dropout", "epochs", "learn_rate", "activation", "mixture"),
                 default = c("3L", "0.001", "0.0", "100L", "0.01", "'relu'", "0.0"))

spec <-
  mlp(
    hidden_units = tune(),
    penalty = tune(),
    dropout = tune(),
    epochs = tune(),
    learn_rate = tune(),
    activation = tune()
  ) %>% 
  set_engine("brulee_two_layer")

param <- spec %>% make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

The use of the L1 penalty (a.k.a. the lasso penalty) does not force parameters to be strictly zero (as it does in packages such as glmnet). The zeroing out of parameters is a specific feature the optimization method used in those packages.

Both penalty and dropout should be not be used in the same model.

Other engine arguments of interest:

• hidden_layer_2 and activation_2 control the format of the second layer.
• momentum: A number used to use historical gradient information during optimization.
• batch_size: An integer for the number of training set points in each batch.
• class_weights: Numeric class weights. See [brulee::brulee_mlp()].
• stop_iter: A non-negative integer for how many iterations with no improvement before stopping. (default: 5L).
• rate_schedule: A function to change the learning rate over epochs. See [brulee::schedule_decay_time()] for details.

Translation from parsnip to the original package (regression)

mlp(
  hidden_units = integer(1),
  penalty = double(1),
  dropout = double(1),
  epochs = integer(1),
  learn_rate = double(1),
  activation = character(1)
) %>%
  set_engine("brulee_two_layer",
             hidden_units_2 = integer(1),
             activation_2 = character(1)) %>% 
  set_mode("regression") %>% 
  translate()

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

Translation from parsnip to the original package (classification)

mlp(
  hidden_units = integer(1),
  penalty = double(1),
  dropout = double(1),
  epochs = integer(1),
  learn_rate = double(1),
  activation = character(1)
) %>% 
  set_engine("brulee_two_layer",
             hidden_units_2 = integer(1),
             activation_2 = character(1)) %>% 
  set_mode("classification") %>% 
  translate()

Preprocessing requirements

Case weights

References

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

parsnip documentation built on April 4, 2025, 1:53 a.m.