r descr_models("bag_mlp", "nnet")
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()].
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()
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()
Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
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