`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

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).

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

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

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.model_spec()`

, 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.

Luraschi, J, K Kuo, and E Ruiz. 2019.

*Mastering nnet with R*. O’Reilly MediaHastie, T, R Tibshirani, and M Wainwright. 2015.

*Statistical Learning with Sparsity*. CRC Press.Kuhn, M, and K Johnson. 2013.

*Applied Predictive Modeling*. Springer.