In parsnip: A Common API to Modeling and Analysis Functions

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r descr_models("logistic_reg", "LiblineaR")

Tuning Parameters

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defaults <- 
  tibble::tibble(parsnip = c("penalty", "mixture"),
                 default = c("see below", "0"))

param <-
logistic_reg() |> 
  set_engine("LiblineaR") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

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param$item

For LiblineaR models, the value for mixture can either be 0 (for ridge) or 1 (for lasso) but not other intermediate values. In the [LiblineaR::LiblineaR()] documentation, these correspond to types 0 (L2-regularized) and 6 (L1-regularized).

Be aware that the LiblineaR engine regularizes the intercept. Other regularized regression models do not, which will result in different parameter estimates.

Translation from parsnip to the original package

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logistic_reg(penalty = double(1), mixture = double(1)) |> 
  set_engine("LiblineaR") |> 
  translate()

Preprocessing requirements

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Sparse Data

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Examples

The "Fitting and Predicting with parsnip" article contains examples for logistic_reg() with the "LiblineaR" engine.

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.