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

r descr_models("multinom_reg", "nnet")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("penalty"),
                 default = c("0.0"))

param <-
  multinom_reg() %>% 
  set_engine("nnet") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

For penalty, the amount of regularization includes only the L2 penalty (i.e., ridge or weight decay).

Translation from parsnip to the original package

multinom_reg(penalty = double(1)) %>% 
  set_engine("nnet") %>% 
  translate()

Preprocessing requirements

Examples

The "Fitting and Predicting with parsnip" article contains examples for multinom_reg() with the "nnet" engine.

Case weights

Saving fitted model objects

References

Luraschi, J, K Kuo, and E Ruiz. 2019. Mastering nnet with R. O'Reilly Media
Hastie, T, R Tibshirani, and M Wainwright. 2015. Statistical Learning with Sparsity. CRC Press.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.