In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("bag_mlp", "nnet")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("penalty", "hidden_units", "epochs"),
                 default = c("0.0", "10L", "1000L"))

param <-
 bag_mlp() %>% 
  set_engine("nnet") %>% 
  set_mode("regression") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

These defaults are set by the baguette package and are different than those in [nnet::nnet()].

Translation from parsnip to the original package (classification)

r uses_extension("bag_mlp", "nnet", "classification")

library(baguette)

bag_mlp(penalty = double(1), hidden_units = integer(1)) %>% 
  set_engine("nnet") %>% 
  set_mode("classification") %>% 
  translate()

Translation from parsnip to the original package (regression)

r uses_extension("bag_mlp", "nnet", "regression")

library(baguette)

bag_mlp(penalty = double(1), hidden_units = integer(1)) %>% 
  set_engine("nnet") %>% 
  set_mode("regression") %>% 
  translate()

Preprocessing requirements

Case weights

References

• Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140

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

parsnip documentation built on Aug. 18, 2023, 1:07 a.m.