details_multinom_reg_nnet: Multinomial regression via nnet

details_multinom_reg_nnetR Documentation

Multinomial regression via nnet


nnet::multinom() fits a model that uses linear predictors to predict multiclass data using the multinomial distribution.


For this engine, there is a single mode: classification

Tuning Parameters

This model has 1 tuning parameters:

  • penalty: Amount of Regularization (type: double, default: 0.0)

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") %>% 
## Multinomial Regression Model Specification (classification)
## Main Arguments:
##   penalty = double(1)
## Computational engine: nnet 
## Model fit template:
## nnet::multinom(formula = missing_arg(), data = missing_arg(), 
##     decay = double(1), trace = FALSE)

Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via fit(), parsnip will convert factor columns to indicators.

Predictors should have the same scale. One way to achieve this is to center and scale each so that each predictor has mean zero and a variance of one.


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

Case weights

The underlying model implementation does not allow for case weights.

Saving fitted model objects

This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.


  • 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.

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