Nothing
r descr_models("mlp", "nnet")
defaults <- tibble::tibble(parsnip = c("hidden_units", "penalty", "epochs"), default = c("none", "0.0", "100L")) param <- mlp() %>% set_engine("nnet") %>% make_parameter_list(defaults)
This model has r nrow(param)
tuning parameters:
param$item
Note that, in [nnet::nnet()], the maximum number of parameters is an argument with a fairly low value of maxit = 1000
. For some models, you may need to pass this value in via [set_engine()] so that the model does not fail.
mlp( hidden_units = integer(1), penalty = double(1), epochs = integer(1) ) %>% set_engine("nnet") %>% set_mode("regression") %>% translate()
Note that parsnip automatically sets linear activation in the last layer.
mlp( hidden_units = integer(1), penalty = double(1), epochs = integer(1) ) %>% set_engine("nnet") %>% set_mode("classification") %>% translate()
The "Fitting and Predicting with parsnip" article contains examples for mlp()
with the "nnet"
engine.
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