In parsnip: A Common API to Modeling and Analysis Functions

#| child: aaa.Rmd
#| include: false

r descr_models("multinom_reg", "glmnet")

Tuning Parameters

#| label: glmnet-param-info
#| echo: false
defaults <- 
  tibble::tibble(parsnip = c("penalty", "mixture"),
                 default = c("see below", "1.0"))

param <-
multinom_reg() |> 
  set_engine("glmnet") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

#| label: glmnet-param-list
#| echo: false
#| results: asis
param$item

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

#| label: glmnet-cls
multinom_reg(penalty = double(1), mixture = double(1)) |> 
  set_engine("glmnet") |> 
  translate()

Preprocessing requirements

#| child: template-makes-dummies.Rmd

#| child: template-same-scale.Rmd

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

#| child: template-uses-case-weights.Rmd

Sparse Data

#| child: template-uses-sparse-data.Rmd

Saving fitted model objects

#| child: template-butcher.Rmd

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.