`nnet::multinom()`

fits a model that uses linear predictors to predict
multiclass data using the multinomial distribution.

## Details

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") %>%
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(),
## 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.

### Examples

The “Fitting and Predicting with parsnip” article contains
examples
for `multinom_reg()`

with the `"nnet"`

engine.

### 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.