man/rmd/multinom_reg_glmnet.md

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

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") %>% 
  translate()

## 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 \code{\link[=fit.model_spec]{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.

Examples

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 tidymodels.org.

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

Sparse Data

This model can utilize sparse data during model fitting and prediction. Both sparse matrices such as dgCMatrix from the Matrix package and sparse tibbles from the sparsevctrs package are supported. See [sparse_data] for more information.

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.

References

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

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

tidymodels/parsnip documentation built on Feb. 19, 2025, 2:10 a.m.