In topepo/parsnip: A Common API to Modeling and Analysis Functions

r descr_models("mlp", "h2o")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("hidden_units", "penalty", "dropout", "epochs", "learn_rate", "activation"),
                 default = c("200L", "0.0", "0.5", "10", "0.005", "'see below'"))

param <-
  mlp() %>% 
  set_engine("h2o") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

The naming of activation functions in [h2o::h2o.deeplearning()] differs from parsnip's conventions. Currently, only "relu" and "tanh" are supported and will be converted internally to "Rectifier" and "Tanh" passed to the fitting function.

penalty corresponds to l2 penalty. [h2o::h2o.deeplearning()] also supports specifying the l1 penalty directly with the engine argument l1.

Other engine arguments of interest:

stopping_rounds controls early stopping rounds based on the convergence of another engine parameter stopping_metric. By default, [h2o::h2o.deeplearning] stops training if simple moving average of length 5 of the stopping_metric does not improve for 5 scoring events. This is mostly useful when used alongside the engine parameter validation, which is the proportion of train-validation split, parsnip will split and pass the two data frames to h2o. Then [h2o::h2o.deeplearning] will evaluate the metric and early stopping criteria on the validation set.
h2o uses a 50% dropout ratio controlled by dropout for hidden layers by default. [h2o::h2o.deeplearning()] provides an engine argument input_dropout_ratio for dropout ratios in the input layer, which defaults to 0.

Translation from parsnip to the original package (regression)

[agua::h2o_train_mlp] is a wrapper around [h2o::h2o.deeplearning()].

mlp(
  hidden_units = integer(1),
  penalty = double(1),
  dropout = double(1),
  epochs = integer(1),
  learn_rate = double(1),
  activation = character(1)
) %>%  
  set_engine("h2o") %>% 
  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),
  learn_rate = double(1),
  activation = character(1)
) %>% 
  set_engine("h2o") %>% 
  set_mode("classification") %>% 
  translate()