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glmnet::glmnet() 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 2 tuning parameters:

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

  • mixture: Proportion of Lasso Penalty (type: double, default: 1.0)

The penalty parameter has no default and requires a single numeric value. For more details about this, and the glmnet model in general, see glmnet-details. As for mixture:

  • mixture = 1 specifies a pure lasso model,

  • mixture = 0 specifies a ridge regression model, and

  • 0 < mixture < 1 specifies an elastic net model, interpolating lasso and ridge.

Translation from parsnip to the original package

multinom_reg(penalty = double(1), mixture = double(1)) %>% 
  set_engine("glmnet") %>% 

## Multinomial Regression Model Specification (classification)
## Main Arguments:
##   penalty = 0
##   mixture = double(1)
## Computational engine: glmnet 
## Model fit template:
## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     alpha = double(1), family = "multinomial")

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. By default, glmnet::glmnet() uses the argument standardize = TRUE to center and scale the data.


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

Case weights

This model can utilize case weights during model fitting. To use them, see the documentation in case_weights and the examples on

The fit() and fit_xy() arguments have arguments called case_weights that expect vectors of 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.


  • Hastie, T, R Tibshirani, and M Wainwright. 2015. Statistical Learning with Sparsity. CRC Press.

  • Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.